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Computer Science Computational Engineering

cs.CE Watch Computational Engineering

Wednesday 9 September

Computational Engineering

Joint nonlinearity in a stiffened aluminium wingbox panel and what it requires of a reduced basis

Nikolaos D. Tantaroudas and Evangelos Papatheou

A shell finite-element model of a stiffened aluminium wingbox panel, a laboratory structure shared by several experimental studies, is built with its plate-to-stiffener joints at the $78$ physical fastener positions and calibrated against measured modal data, a six-parameter sensitivity update of the substructure moduli within $\pm20\%$ reproducing the first five measured modes to $1.1$–$4.3\%$ with modal-assurance values of $0.88$–$1.00$. Making the fasteners nonlinear, at equal mesh, mass and damping, shows that joint nonlinearity enters the response strongly asymmetrically. Hardening is almost invisible, at most $+1\%$ in frequency, whereas softening or slipping the joints moves the first three resonances by $-2.1$, $-4.9$ and $-9.2\%$, and friction removes up to $80\%$ of the resonant peak before it recovers. Newmark integration of all $18{,}804$ degrees of freedom and a harmonic balance condensed exactly onto the joints and continued in arclength agree to $5.5\times10^{-4}$. A projection-based nonlinear model order reduction then locates the criterion that a jointed structure imposes on a reduced basis. The binding quantity is not the linear response, which eleven vectors reproduce to better than $0.01\%$, but the receptance of the structure between the joints, of which $126$ global eigenvectors carry about $2\%$, and without which the reduced model overpredicts the hardening shift of the fundamental by a factor of thirty and returns a value beyond the rigid-joint limit of the panel.
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Computational Engineering · Cross-listed from cs.RO

MuJoCable: Reduced-Order Surface-Routed Cable Transmission for Tendon-Driven Robots

Yi Zhang, Qi Shao, Yicong Lin et al. (+3 more)

Tendon transmissions reduce distal inertia and add compliance, yet routing, slack, and friction govern motion and force transfer. Mainstream rigid-body robotics simulators such as MuJoCo do not jointly resolve moving noncircular contact, unilateral tension, and segment friction. We present MuJoCable, which adds a reduced-order, configuration-dependent cable transmission to MuJoCo. Its routing algorithm jointly optimizes an ordered path across moving analytic and mesh surfaces. A unilateral axial law, directional Capstan propagation, and nodal virtual work map this path to segment tensions and body forces. The warm-started engine plugin applies these forces during simulation and exposes route and load states for design. Pulley benchmarks recover analytical transmission relations with a Capstan-ratio error below 0.5%. On the underactuated 18-joint SpiRobs, MuJoCable reveals friction-driven load growth and proximal redistribution of joint rotation that the native tendon does not represent. Hardware tests on SpiRobs and a tendon-route-coupled finger reproduce observed motion sequences. By making physical threading executable, MuJoCable brings transmission sources of the simulation-to-reality gap into route, cable, and actuator design before fabrication.
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All authors Yi Zhang, Qi Shao, Yicong Lin, Muyuan Ma, Tao Sun, Yue Xie

Computational Engineering

Geometric organization of olfactory descriptor data in the Poincaré disk

Aniss Aiman Medbouhi, Farzaneh Taleb, Giovanni Luca Marchetti et al. (+1 more)

Odor quality is commonly represented using high dimensional descriptor profiles, yet their low dimensional organization remains unclear. We investigated whether a two-dimensional hyperbolic embedding can provide an interpretable representation of this structure. We applied hyperbolic metric multidimensional scaling to two complementary datasets: 480 Sagar rating profiles from three participants rating 160 odorants on 15 continuous descriptors, and 4983 GoodScents–Leffingwell molecules annotated with 138 binary descriptors. The embeddings substantially preserved pairwise descriptor distances, supporting subsequent analyses of radial and angular organization. In Sagar, rating profile entropy was strongly and negatively associated with hyperbolic radius, with diffuse profiles closer to the center and concentrated profiles closer to the boundary. This radial organization emerged primarily at the level of the full descriptor profile, rather than any individual descriptor, and remained robust across alternative descriptor representations, participant specific analyses, and averaged ratings. Sweet, musky, fruity, pleasantness showed the strongest directional trends. In GoodScents–Leffingwell, active label entropy, reflecting descriptor multiplicity, increased with radius, whereas orthogonalized descriptor entropy, reflecting spread across orthogonal modes, decreased with radius. Related binary descriptors occupied coherent localized high-density regions. These findings reveal complementary radial and angular organization in the hyperbolic representation of olfactory descriptor data. They support hyperbolic mapping as an interpretable descriptive framework in which radius summarizes global profile properties, while the angular component captures continuous descriptor gradients and categorical organization.
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All authors Aniss Aiman Medbouhi, Farzaneh Taleb, Giovanni Luca Marchetti, Danica Kragic

Tuesday 8 September

Computational Engineering · Cross-listed from cs.LG

Tensor-Train Weak SINDy: Identifying High-Dimensional Nonlinear Dynamics

Will Houser, Vanja Dukic and David M. Bortz

Weak Sparse Identification of Nonlinear Dynamics (WSINDy) provides a noise-robust approach for learning dynamical systems from data without requiring numerical differentiation. However, for high-dimensional systems, tensor-product libraries of candidate functions grow exponentially with the state dimension, making standard WSINDy expensive in both computation and memory. The Multidimensional Approximation of Nonlinear Dynamics (MANDy) addresses this scaling through a tensor-train (TT) representation of the candidate library, but does not provide a mechanism for sparse model selection. Here, we combine these approaches to develop TT-WSINDy, which performs the weak-form transformation, regression, and sparsification in TT format. We show that the TT formulation recovers the corresponding WSINDy regression problem and derive polynomial time and memory complexity bounds for the tensor-train sparsification procedure. Numerical experiments demonstrate robustness to measurement noise and computational savings for high-dimensional systems.
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Computational Engineering · Cross-listed from cs.AI

Decision-Focused Active Learning for Scale-Aware Critical-Materials Recovery

Niranjan Srinivas, Debajyoti Ray and Elias Nakouzi

Choosing a recovery process for scale-up requires connecting laboratory results with product requirements, process costs, and scale effects. We analyze records from Pacific Northwest National Laboratory's Computer Intelligence for Critical Element Recovery and Optimization (CICERO) workflow for autonomous selective precipitation. Active learning uses prior results to choose experiments. In a conditional retrospective benchmark with fitted models and recycled neodymium-iron-boron (NdFeB) magnet records, active learning finds the best recorded result with fewer experiments than nonadaptive space filling. Enrichment is the selected rare-earth-to-iron ratio relative to that in the feed. Adaptive policies reach the recorded enrichment maximum by 16 to 24 wells (individual experiments), versus 48. Our two-stage reconstruction ties two adaptive alternatives at 16 wells. Conditional analyses of recycled samarium-cobalt (SmCo) magnets show a Round 2 tradeoff between purity and nominal yield, the recovery fraction calculated from an assumed starting amount - NdFeB Round 1 routes differ in enrichment. Rankings for produced water from oil and gas extraction depend on phase and dilution assumptions requiring confirmation. We propose choosing batches by their expected reduction in downstream Bayes risk: the minimum expected loss among available process decisions under current beliefs. In exploratory simulations, a hybrid that filters candidates has lower estimated loss than the implemented joint search across routes and conditions. Differences involving the synthetic two-stage policy are small relative to estimation uncertainty. We outline a pre-registered prospective test under a shared loss and logging standard, requiring clarified measurements and records, a defined process decision and relevant outputs, credible economic inputs, and validation at the intended scale.
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Computational Engineering

Fixed-mesh based approach for modeling of superconducting magnetic bearings

Elias Paakkunainen, Bárbara Maria Oliveira Santos, Gabriel dos Santos et al. (+3 more)

Numerical simulations of superconducting magnetic bearings (SMBs) are complicated by the relative motion between high temperature superconductors and the magnetic guideway, which commonly requires repeated updates of the geometry and mesh. We present a fixed-mesh based approach for modeling the motion induced effects in which a coordinate transformation is applied only to the air region between the moving regions. The transformation can be included in a time-dependent anisotropic material tensor, leaving the mesh during the simulation and the rest of the formulation unchanged. To provide numerical examples, the approach is implemented in 2D using the finite element method with the $j$-$a$ formulation and is combined with homogenization and circuit coupling for double-crossed loops of coated conductors. The open-source model implementations are made available. The proposed approach is verified and validated against existing modeling approaches and experimental results from the literature for SMBs. Two simulation examples are presented, and in both of them the levitation force predicted by the fixed-mesh model agrees with the experimental results, yielding $R^2$ values above 0.99. For a verification problem, it is shown that the fixed-mesh model converges towards the remeshed reference solution under mesh refinement as expected. For an SMB undergoing vertical and horizontal translations, the homogenized fixed-mesh model reproduces the lateral-force response of a fully resolved moving-geometry reference model. Additionally, the homogenized fixed-mesh model is computationally more efficient than the reference model, indicating a significant reduction in the number of degrees of freedom and the computation time. The results demonstrate accurate and computationally efficient simulation of translational motion with the fixed-mesh based approach.
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All authors Elias Paakkunainen, Bárbara Maria Oliveira Santos, Gabriel dos Santos, Timo Tarhasaari, Paavo Rasilo, Sebastian Schöps

Computational Engineering

Risk-Aware Generative Inpainting for Optimized Design Editing of EV Battery Cooling Channels

Leekyo Jeong, Yoon Koo Lee and Namwoo Kang

Cooling-channel layouts for electric-vehicle battery packs must deliver temperature uniformity and low pressure drop while maintaining a single continuous channel. In late-stage design, local modification is a practical way to improve performance while retaining established global features. Diffusion-based inpainting supports such local edits, but its stochastic nature produces different outcomes even for the same region. This raises a fundamental question: how should edit locations be selected when each modification's outcome is stochastic? We propose a risk-aware generative editing framework that selects edit locations by accounting for this variability. Rather than scoring each candidate location by a single expected improvement, the method estimates a distribution of outcomes from offline edit results evaluated with a CFD-trained surrogate, and ranks locations by a chosen risk level. A single trained model therefore supports different editing preferences at inference time, emphasizing either higher expected improvement or greater consistency, without retraining. The policy is evaluated against random editing in a held-out paired study across seven mask configurations. It improves the cooling-channel objective over random editing in most configurations, and the advantage holds under independent CFD verification of the edited designs. Varying the risk level reveals a consistent trade-off between mean improvement and run-to-run consistency, while the learned distribution is useful for ranking locations but should not be read as a calibrated probability distribution. Together, these results show that stochastic generative editing can be converted from a source of variability into a controllable design decision through risk-aware location selection: effective editing depends not only on how a design is modified, but also on where and how much outcome variability is acceptable.
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Computational Engineering

Kirigami Meta-Sheet for Enhanced Impact Absorption

Dahyun Joo, Yasuhiro Miyazawa, Seokhyeon Hong et al. (+3 more)

Impact absorbers based on mechanical metamaterials often use bulky, vertically stacked architectures, limiting large area deployment and scalable manufacturing. Here, we propose a kirigami meta-sheet as a planar absorber that uses transitions between positive and negative stiffness regimes rather than sacrificial crushing. Guided by an analysis of a simple mass-spring-damper model, we program the stiffness of kirigami meta-sheets through the hinge ratio connecting the unit cells. Quasistatic indentation experiments confirm that the meta-sheet with a low hinge ratio most clearly exhibits the negative stiffness transition. The drop-tower tests show that it reduces rebound, decreases the first impact force, and increases dissipation. Unlike polyethylene mesh and styrofoam, this kirigami meta-sheet is shown to be effective in protecting a falling egg. Its planar geometry enables area scaling by tiling and is compatible with sheet level manufacturing routes such as cutting, molding, and lamination, establishing kirigami meta-sheets as practical impact absorbers.
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All authors Dahyun Joo, Yasuhiro Miyazawa, Seokhyeon Hong, Dongha Lee, Jinkyu Yang, Do-Nyun Kim

Computational Engineering · Cross-listed from cs.LG

Learning Metamaterial Eigenmodes with Wavelet-Encoded Fourier Neural Operators

Han Zhang, Alexander Ogren, Cynthia Rudin et al. (+2 more)

Machine learning surrogates based on neural operators have shown broad applicability in solving forward PDE problems. However, eigenvalue problems, in which an eigenparameter and one of several valid eigenmodes must be simultaneously solved, remain difficult because standard operator learning formulations assume a unique input-output map. This work demonstrates that Fourier Neural Operators (FNOs), combined with wavelet-based encodings of PDE inputs, can learn and predict multiple eigenmodes of the elastic wave equation, corresponding to deformation modes of acoustic waves propagating through arbitrary metamaterial geometries. We provide a mechanistic explanation and experimental evidence for why wavelet encodings are well matched to the dual spatial-spectral structure of the FNO, enabling deterministic mode selection on both continuous-valued and binary-valued geometries within a single model, and for why prediction accuracy varies with geometric discontinuities. For metamaterial design, the resulting surrogate accelerates the simulation stage of the design cycle by three orders of magnitude relative to finite element analysis on a consumer-grade CPU, while preserving high fidelity. These results also carry broader implications for designing input encodings in other multi-mode PDE solvers based on spectral neural operators.
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All authors Han Zhang, Alexander Ogren, Cynthia Rudin, Johann Guilleminot, L. Catherine Brinson

Monday 7 September

Computational Engineering · Cross-listed from stat.ME

Risk-Aware Goal-Oriented Bayesian Optimal Experimental Design

John D. Jakeman, Rebekah White, Bart van Bloemen Waanders et al. (+2 more)

Traditional Bayesian optimal experimental design (OED) selects measurements that best inform a model's parameters. However, such measurements can be suboptimal for downstream predictions. Goal-oriented OED targets the prediction directly. However, the existing goal-oriented criteria value all reductions in predictive uncertainty equally, with no way to prioritize rare, high-consequence outcomes. In this article, we develop a risk-aware framework that composes risk at three levels, each generalizing an ingredient of classical $I$- and $G$-optimal design: a deviation measure of the posterior predictive uncertainty (generalizing the predictive variance), a risk measure across the prediction domain (interpolating $I$-optimal averaging and $G$-optimal worst-case selection), and a risk measure over datasets (generalizing the expectation). We generate each level from a regret function in the risk quadrangle, so that one triple specifies a practitioner's risk preference. We relax the design to continuous weights on the unit simplex and construct a nested-quadrature estimator that is differentiable in the design variable. This enables solving the optimal design problem with gradient-based methods, avoiding a combinatorial search over candidate designs. For a linear-Gaussian lognormal model and a nonlinear extension, we derive closed-form objectives. These give exact references against which we verify that the estimator converges. We demonstrate this framework for finding optimal sensor placements in an inverse problem governed by an advection-diffusion equation. We find that the risk-aware designs substantially outperform the expected-information-gain baseline, which is statistically indistinguishable from a random allocation.
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All authors John D. Jakeman, Rebekah White, Bart van Bloemen Waanders, Drew P. Kouri, Alen Alexanderian

Computational Engineering · Cross-listed from cs.AI

From Event Logs to Governed Action: A BlueSky Agenda for Agentic Process Mining

Yiyuan Yang, Zheshun Wu, Yong Chu et al. (+3 more)

Process mining has long turned event logs into process knowledge: discovered models, conformance evidence, bottleneck diagnoses, and runtime predictions. Agentic AI changes the target. Process-aware agents will not only ask what happened. They will ask whether a proposed action should be taken, given the available evidence, privacy budget, organizational authority, and downstream risk. This BlueSky paper proposes event-to-action process mining: a process-mining agenda for transforming heterogeneous operational event data into governed action. The goal is not another dashboard, a generic enterprise simulator, or a language interface over logs. We argue that the community needs four mineable artifacts: event-object representations, action evidence packages, governance contracts, and benchmarks where act, defer, ask, and refuse are all valid outputs. This agenda is timely because agentic business process management (BPM), LLM-assisted process mining, object-centric event standards, causal process monitoring, and privacy-preserving learning are maturing separately. Bringing them together defines a data-mining target inside process mining: mining logged organizational behavior for accountable action, not only retrospective insight.
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All authors Yiyuan Yang, Zheshun Wu, Yong Chu, Zhenghua Chen, Zenglin Xu, Qingsong Wen

Computational Engineering

Buildability Assessment of 3D-Printed Concrete Structures Using Smoothed Mohr-Coulomb Plasticity with Isotropic Hardening

Saif-Ur-Rehman, Annika Robens-Radermacher, R. J. M. Wolfs et al. (+1 more)

Extrusion-based 3D concrete printing builds structural elements layer upon layer without formwork, so freshly deposited material must carry the growing weight of the structure while it is still gaining strength and stiffness. The largest number of layers that can be printed before failure, the buildability, is therefore central to process design. Its prediction rests above all on how accurately the constitutive model represents fresh concrete. Existing finite element frameworks pair gradual pre-failure hardening only with smooth yield surfaces, which leaves the Mohr-Coulomb surface that best describes fresh concrete restricted to perfect plasticity. This work combines a smoothed Mohr-Coulomb yield surface with nonlinear isotropic hardening of the cohesion, formulated in an updated Lagrangian setting with an activation field for layer deposition. Failure is identified from a tangent-stiffness eigenvalue criterion that matches the analytical self-weight buckling load of a slender wall and is insensitive to geometric imperfection. The framework reproduces the measured collapse of a printed straight wall within one layer and predicts the collapse of a hollow cylinder closer to experiment than existing frameworks. A study over six diameters and six hardening rates shows buildability peaking at intermediate diameters and rising with the hardening rate. Recording how much of its strength the material has used at failure, slender cylinders fail with much still in reserve while intermediate ones use nearly all of it. The two failure modes are therefore identified as the limits of one process rather than separate failure mechanisms.
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All authors Saif-Ur-Rehman, Annika Robens-Radermacher, R. J. M. Wolfs, Jörg F. Unger

Computational Engineering

Your Agent Says Yes: Interpreting Adversarial Market Behavior Beyond Individual Transactions

Zelin Li, Yiyun Su, Matt White et al. (+3 more)

Transaction-local controls answer whether one financial request may proceed, but market behavior can be distributed across messages, agents, assets, and time. We study this interpretation gap in a virtual exchange populated by ten role-conditioned language-model agents. The agents communicate, trade reference assets and futures, launch tokens, and manage concentrated-liquidity pools under prescriptive adversarial roles. We analyze eight 72-cycle trajectories across two time-blinded hourly replay paths, with a runner-side wallet policy enabled or disabled. The retained artifacts connect generated outgoing messages, policy events, balances, positions, and cycle-end market state. A focal reconstruction shows a launch–promotion–exit scenario realized across private coordination, public claims, follower positioning, repeatedly withheld exits, and a later non-blocking request aligned with a token balance change. Across policy-enabled runs, the gate withholds direct requests selectively; most policy-categorized candidates are flagged rather than blocked, while the surrounding interaction can continue. Repeated runs also show that category-level and within-trajectory relations can recur even when normalized score-change rankings do not. These findings motivate agent-behavior evaluation that links communication, authorization, and evolving state instead of treating individual transaction verdicts as complete safety judgments.
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All authors Zelin Li, Yiyun Su, Matt White, Zhipeng Wang, Xiao-Yang Liu, Tianyu Shi

Computational Engineering

Elastoplastic inherent strain-based topology optimization for residual stress reduction in metal additive manufacturing

Takao Miki, Jike Han, Kazuhiro Izui et al. (+1 more)

This paper proposes a topology optimization method for reducing the residual stress arising in the building process of metal additive manufacturing. First, a layer-by-layer process analysis model based on an elastoplastic inherent strain method is introduced. In this model, the incremental displacement is solved anew at each layer step, and the stress history is explicitly incorporated into the constitutive equation as the stress accumulated up to the previous step, which guarantees the stress continuity across layer interfaces without introducing activation strains. Next, the design sensitivity of this analysis model is derived based on the adjoint method. Taking the pair of the stress and the equivalent plastic strain as the state variables reduces the dependency between layer steps to a one-step recurrence, and the adjoint fields are constructed as a layer-by-layer reverse sweep that reuses the coefficient tensors obtained in the forward analysis. Consequently, the cost of the sensitivity analysis scales linearly with the number of layers and remains of the same order as that of the forward analysis. An optimization problem is then formulated based on the density method to minimize the P-norm of the residual stress at the completion of the building process under the volume and final-use compliance constraints, and the derived sensitivities are verified by comparison with central finite differences. Finally, the proposed method is demonstrated through two- and three-dimensional examples of residual stress minimization under a compliance constraint. The results clarify that, under the elastoplastic analysis, the maximum residual stress is bounded by the yield surface, and the optimization therefore reduces the extent of the yielded and plastic strain accumulating regions rather than the peak stress value.
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All authors Takao Miki, Jike Han, Kazuhiro Izui, Shinji Nishiwaki

Computational Engineering · Cross-listed from cs.LG

Protocol effects on feature-based hardware-Trojan detection across Trust-Hub families

Hang Xiao, Chuhong Xu, Kainan Zhou et al. (+2 more)

Trust-Hub reuses host circuits: several files differ mainly in the inserted Trojan. When gates from sibling variants enter both training and test folds, a detector can benefit from host logic it has already seen. We measure that effect instead of proposing another classifier. The corpus contains 49,124 gates from 16 netlists grouped into five host families. We left the parser, 36 gate features, class weighting, model settings, threshold, and family-level aggregation unchanged and altered one choice: the test boundary. The three settings draw test gates from the pooled corpus, withhold a complete netlist, or withhold every variant of one host. The choice matters. Random forest records F1/AP of 0.914/0.978 with pooled gates, 0.636/0.851 with one netlist held out, and 0.460/0.577 with a host family held out. XGBoost falls from 0.946/0.976 to 0.464/0.544 across the same comparison. Logistic regression loses AP, although its fixed-threshold F1 is not monotonic. Each family shows the same pooled-to-family direction. Feature removal, repeated model and simulator seeds, score normalization, parser-related exclusions, and a smaller sample change the size of the gap without reversing it. Aggregation also matters: a gate-weighted average is dominated by the larger ISCAS files, so the headline values give each host family one vote. Bootstrap and jackknife summaries keep the gap positive, but their folds reuse training families. We treat the five family rows as descriptive evidence rather than independent trials. Five host families are too few for a population claim, and the experiment says nothing about transfer to a new cell library or an industrial design. It supports a narrower conclusion: sibling benchmark variants can inflate apparent transfer. Benchmarks with several variants of one host circuit should report family-aware holdouts and all five family results beside pooled scores.
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All authors Hang Xiao, Chuhong Xu, Kainan Zhou, Gangzhen Qian, Lu Yi

Computational Engineering

Centering Drives Normalization Gains: Price-Offset Nuisances in Cross-Sectional Return Prediction

Mingju Chen, Qianhui Liu, Yui Lo et al. (+1 more)

Cross-sectional return prediction from raw intraday bars is sensitive to each instrument price level, an additive nuisance under a return-ranking hypothesis. We test whether removing this offset, rather than rescaling amplitudes or changing the encoder, explains gains on a point-in-time CSI~300 five-minute panel. We evaluate eight parameter-matched encoders with and without RevIN normalization; a parameter-free ladder then separates identity, scale-only, centering, last-value referencing, differencing, and standardization across all fields and restricted channels. Centering drives the reliable effect, while scale-only normalization does not help. All eight paired effects are positive and survive Holm correction on raw rank IC, after style residualization, and after further residualizing on short-term reversal. Among six stronger encoders, gains of 0.0376-0.0567 exceed the 0.0109 spread of normalized IC (0.0830-0.0939). Price-only standardization retains 93–101% of the all-field gain. These results place the main effect in transformed price-channel offset removal rather than amplitude scaling or encoder choice.
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All authors Mingju Chen, Qianhui Liu, Yui Lo, Yuanhang Liu

Sunday 6 September

Computational Engineering · Cross-listed from eess.IV

MedGSSR: Generalizable Medical Image Super-Resolution 3D Reconstruction via Hierarchical Feed-forward Gaussian Splatting

Chengkai Wang, Luoyu Hong, Yiting Zhao et al. (+7 more)

High-resolution volumetric medical imaging is critical for clinical diagnosis, yet acquisition is often limited by scanner hardware, scan time, and for CT, radiation dose. Medical 3D Super-Resolution (Med3DSR) offers a computational alternative, but existing methods commonly rely on per-subject optimization, pretrained priors, or coordinate-based implicit representations, which compromise anatomical fidelity and limit efficiency. To address these limitations, we present MedGSSR, a fully end-to-end feed-forward framework that represents volumes as an explicit 3D Gaussian field for Med3DSR. Unlike coordinate-based implicit functions, our explicit 3D Gaussian representation naturally enhances signal continuity and local high-frequency fidelity. Specifically, MedGSSR explicitly decouples the reconstruction process into coarse-grained structural preservation and fine-grained textural refinement through the proposed Pyramid Anatomical Encoder and a Hierarchical Gaussian Projector. To support arbitrary-scale super-resolution, we introduce sub-voxel Gaussian decomposition and a Differentiable Gaussian Voxelizer that directly queries the continuous 3D intensity field, reducing discretization artifacts. Extensive experiments on MRI and CT benchmarks demonstrate that MedGSSR significantly outperforms state-of-the-art methods. Notably, our framework exhibits robust generalizability across unseen datasets without requiring per-subject optimization, enabling fast inference and high-fidelity volumetric super-resolution in practical clinical settings. Our project webpage, including code, is at https://william2ai.github.io/medgssr
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All authors Chengkai Wang, Luoyu Hong, Yiting Zhao, Jiamin Wang, Xiang Feng, Feiwei Qin, Zhenzhong Kuang, Xuefei Yin, Ali Bashashati, Yanming Zhu

Computational Engineering · Cross-listed from math.NA

A review of weakly enforced Dirichlet boundary conditions in computational flow analysis

Ming-Chen Hsu, Monu Jaiswal and Yuri Bazilevs

Strongly enforced Dirichlet boundary conditions require highly refined near-wall meshes to resolve steep velocity and thermal gradients. This introduces high computational costs, especially for practical flow simulations. Weakly enforced boundary conditions alleviate this burden by acting as a variationally consistent near-wall model. By allowing a controlled slip at the solid wall, weak enforcement recovers accurate flow quantities on coarse boundary-layer meshes across both incompressible and compressible regimes. Furthermore, weak boundary conditions serve as the fundamental enabling technology for immersogeometric analysis. Because the weak operator evaluates boundary integrals independently of the background mesh, high-fidelity flow analysis can be performed directly on complex geometries without fitting a mesh to the surface. This capability has facilitated direct geometry-to-analysis workflows for boundary-representation CAD models, raw point clouds, photogrammetric reconstructions, and segmented medical images. This review examines the unified mathematical development of the weak boundary condition framework from scalar advection-diffusion equations to the full Navier-Stokes equations, illustrating its versatility and robustness across incompressible and compressible flows, whether in traditional boundary-fitted, sliding-interface, or advanced immersogeometric applications.
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Friday 4 September

Computational Engineering

MiDShip: Multimodal Dataset of Ship Cargo Hold Structures for Engineering Design

Noah J. Bagazinski, Md. Ferdous Alam, Jaya Manideep Rebbagondla et al. (+1 more)

Ship structures govern vessel strength, safety, and manufacturability, but their design must satisfy hundreds of classification society requirements, making the process complex and iterative. Data-driven approaches are limited by the lack of structured datasets linking design geometry, structural performance, and rule-based constraints. This paper presents MiDShip, a multimodal dataset of 12,753 synthetic cargo-hold structural designs: 6,020 random, 496 generated by an SGLD-inspired procedure, and 6,237 generated by an equation-informed repair procedure. Each design includes parametric data, full and mesh-ready 3D geometry, engineering drawings and annotations, a bill of materials, and preliminary structural evaluations. Twenty-five constraints derived from a subset of ABS MVR are also evaluated. None of the random designs satisfies all constraints. Among the SGLD-inspired designs, 322 (64.9%) were fully compliant, with an average of 0.409 violations, 82.7% below the seed mean and 96.9% below the random-design mean. The repair procedure, developed through LLM-assisted code analysis, produced 4,952 fully compliant designs (79.4%), averaging 0.296 violations, 97.1% below the paired-source mean. In equal-size comparisons, mean nearest-neighbor distances in the scaled 120-parameter space were 3.495 for repaired designs, 1.144 for SGLD batches, and 3.729 for random designs. The primary contribution is the synchronized dataset and its generation and evaluation infrastructure; the generation studies demonstrate its utility rather than proposing new optimization algorithms. MiDShip supports machine learning, generative design, and automated rule-based evaluation for ship structures.
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All authors Noah J. Bagazinski, Md. Ferdous Alam, Jaya Manideep Rebbagondla, Faez Ahmed

Computational Engineering

Agentic AI for Staged Three-Dimensional Finite-Element Tunnel Modelling

Ochok Duangsano, Kuo-Chieh Chao, Sompote Youwai et al. (+3 more)

Three-dimensional finite-element (FE) analysis gives the most complete picture of tunnelling-induced ground movement, but it is slow: an engineer must drive the PLAXIS 3D interface through project set-up, stratigraphy, materials, structural elements, meshing and staged calculation. This paper presents a pipeline in which a large language model (LLM) drives PLAXIS 3D through the Model Context Protocol (MCP), with no human operating the interface. A purpose-built MCP server exposes the official remote-scripting API as 19 typed tools. Four agents share the work: orchestrator, geometry, calculation and verification. They draw on 15 inspectable domain-knowledge skill modules and a provenance-tagged intake stage. The verification agent is information-barriered, auditing every model against the raw specification at a pre-mesh checkpoint and again as a calculation gate. It was evaluated on 12 single-tunnel problems in Bangkok subsoil across four capability tiers, using a 15-arm ablation matrix; 13 automated arms and a manual expert baseline were executed, one run per cell. The full system built 12 of 12 models at a mean of 6.1 min with a clean audit on every one; the same models took 24.6 min by hand. Domain skills were decisive: with the complete architecture but all 15 modules withheld, no model was produced. Base-model capability acted as a threshold rather than a gradient. Fable 5, Opus 5 and Sonnet 5 each built 12 of 12, while Haiku 4.5 halted on all 12. Among models that built, agreement was exact: 107 of 111 comparable cells reproduced the reference mesh to the element, and 83 of 86 solved cells agreed with the hand-built expert model within 1.01 % in maximum settlement and 1.21 % in trough-width parameter. Every exception traced to a value misread at intake. The reliability problem in agentic FE automation therefore lies in specification reading, not in code generation.
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All authors Ochok Duangsano, Kuo-Chieh Chao, Sompote Youwai, Nattavich Sittiamornporn, Chana Phutthananon, Pornkasem Jongpradist

Computational Engineering

A deflection-basin fusion transformer for predicting critical pavement strains from falling weight deflectometer

Chana Phutthananona, Sompote Youwai and Warat Kongkitkula

The two critical strains of pavement design, under the asphalt layer and on the subgrade, are conventionally obtained by backcalculating layer moduli from a falling weight deflectometer (FWD) basin and propagating them through layered-elastic analysis. That route is ill-posed and confined to the office. We propose the deflection-basin fusion transformer (DBFT), a 0.22M-parameter attention model predicting both strains in one step from the basin and the layer thicknesses, read as a sequence indexed by offset. One objective spans 14,174 layered-elastic solutions and 7,651 field measurements from 96 Thai highway sections, three routes withheld entirely. On exact theory DBFT reaches R^2 >= 0.9998 at RMSE <= 1.24 microstrain, an order of magnitude better than published index equations. Trained on that factorial alone it fails on measured basins, R^2 <= 0.14: the field domain inverts the sign of the outer-basin correlation. The combined objective restores 0.964 and 0.879 on the withheld routes, holding R^2 >= 0.993 on theory. Attention and SHAP recover recognised mechanics, including reliance on the 1200-1800 mm range routine indices never reach. Agreement is with backcalculation-consistent mechanics, not gauge-measured strain. Both strains return in 1.6 ms on one CPU thread.
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Computational Engineering · Cross-listed from cs.AI

What LLM Trading Agents Actually Do in Production: A Six-Month, Population-Scale Record from Two Fleets

T.J. Barton, Chris Constantakis, Patti Hauseman et al. (+4 more)

We present a continuous, population-scale measurement record of autonomous language-model trading agents operating in production across two systems with one design lineage: DX Terminal Pro (3,505 user-funded vaults trading real ETH in Base memecoin markets for 21 days, February to March 2026) and the DXAP live alpha fleet (500 to 599 user-created agents all-history, 91 to 117 concurrently active, trading Hyperliquid perpetuals, June to August 2026). The record spans roughly six months, 7.5M single-model invocations with about 300K onchain actions, and a further 231,638 multi-tool turns producing 14,596 fills. Four findings carry the paper. First, the operating layer determines behavior more than anything written in strategy text: a risk slider explains leverage (+0.425 per level), agent fixed effects absorb 60% of variance, and a leaderboard render boundary causally routes selection (regression discontinuity 1.75x at the top-3 cut). Second, sizing is volatility-blind: median leverage is 5.0x in every volatility sextile, and one posture-slider cell (11% of the book) holds 62% of liquidations. Third, agents capture almost none of the upside they reach: 43.2% of positions saw at least +300 bps of favorable excursion within 24h, yet 49.3% of those closed with a negative trade return; a mechanical bracket recovers +39.0 bps per position. Fourth, neither fleet shows a directional edge. The DXAP fleet is not profitable and trails a matched Hyperliquid retail benchmark (41% vs. 50% roundtrip win rate). A paired-replay league of frontier models on 416 captured production scenarios finds decision quality statistically indistinguishable at this horizon, while choice stability differs sharply across model families. Every headline survives day-clustered inference, permutation nulls, and a common-fee restatement; the paper closes with a 17-rule methodology canon bought with our own retractions.
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All authors T.J. Barton, Chris Constantakis, Patti Hauseman, Annie Mous, Alaska Hoffman, Brian Bergeron, Hunter Goodreau

Thursday 3 September

Computational Engineering

LLM-Aided Design for Manufacturing: A Multi-Agent System for Intent-Preserving Redesign of CAD for Improved Manufacturability

Kojo Welbeck, Xiangyu Shi, Zahra Sadeghi et al. (+2 more)

We introduce autonomous, intent-preserving Design for Manufacturing (DFM) redesign of CAD parts: given an engineer's CAD model, the method returns a variant that is easier to manufacture without losing its design intent. Generating such a redesign in a single shot is unreliable, since CAD fidelity degrades as parts grow complex; we instead produce it as a sequence of individually verified design transitions. Our DFM-Redesign pipeline realizes this with two coupled agent subsystems driven by a pretrained multimodal LLM: a DFM Reviewer that inspects the current design and proposes one intent-preserving manufacturability improvement at a time, and a CAD Modifier that executes each proposal as an edit to the part's CadQuery program. The CAD Modifier closes a verification loop, compiling every candidate edit and visually checking it against the intended change from multi-view renderings, then re-generating or re-instructing until the edit is accepted or abandoned. Iterating review and verified modification compounds edits into parts more complex than one-shot generators reliably produce, preserves the original intent at each step, and requires no fine-tuning. On a 46-part benchmark scored by chamfer distance to reference geometries, the CAD Modifier reproduces target parts more accurately on average than chain-of-thought single agents given the same tools, and ablations isolate the contributions of the visual review loop and of captioning the design state before each edit. A centrifugal pump casing built from 32 chained transitions illustrates the complexity reachable by compounding verified edits. This is a preliminary report: evaluation of the full review-and-redesign loop, including manufacturability gain and an operational measure of intent preservation, is ongoing.
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All authors Kojo Welbeck, Xiangyu Shi, Zahra Sadeghi, Qi Zhu, Ping Guo

Computational Engineering

Influence of Extruded Filament Shape on Buildability in 3D Concrete Printing: A Geometry-Informed Deep Learning-FEM Approach

Giacomo Rizzieri, Saif-Ur-Rehman, Jörg F. Unger et al. (+1 more)

The geometric morphology of deposited filaments can significantly influence the structural performance and stability of 3D concrete-printed (3DCP) structures. However, most finite element (FEM)-based approaches for buildability assessment represent printed layers as simplified rectangles, potentially limiting predictive accuracy. This study proposes a geometry-informed modelling framework that integrates the deep-learning-based filament shape prediction tool ShapeGen3DCP with a layer-activation FEM approach to investigate the effect of realistic filament geometries on buildability. The framework generates geometry-aware numerical models directly from material and process parameters, eliminating the need for experimental filament characterization or computationally intensive fluid-flow simulations. Validation against experimental data and a parametric study of rectilinear walls demonstrate that extrusion parameters and the resulting filament geometry can significantly influence buildability predictions. Realistic filament representations are particularly important for free-flow deposition, whereas layer-pressing strategies are less sensitive to geometric simplifications. Among the investigated representations, an elliptical approximation provides an effective balance between geometric fidelity and modelling simplicity. When rectangular representations are preferred to enable regular computational meshes for faster simulations, defining their dimensions based on volume conservation improves prediction reliability compared with calibrating them using either the maximum filament width or the interlayer contact width. Overall, the proposed methodology demonstrates the importance of incorporating filament geometry into 3DCP simulations and provides practical guidance for selecting efficient and accurate geometric representations for buildability assessment.
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All authors Giacomo Rizzieri, Saif-Ur-Rehman, Jörg F. Unger, Annika Robens-Radermacher

Computational Engineering

Differentiable Hybrid Modelling for Learning and Optimising Chemical Transport Processes from Experimental Data

Arthur Jessop, Mohammed Alsubeihi, Ben Moseley et al. (+1 more)

Transport models underpin the design and optimisation of chemical processes, yet their constitutive laws are typically hand-picked, their experimental initial conditions are assumed exact, and their computational cost restricts optimisation to a handful of process variables. Here we introduce a differentiable hybrid modelling framework that embeds neural networks within a GPU-accelerated population balance equation solver to discover constitutive laws and assimilate initial conditions directly from experimental crystallisation data. Using thirteen real experiments with multi-dimensional size and shape measurements, we show that treating measured initial conditions as exact yields a physically inconsistent rate law despite improved predictive accuracy, whereas assimilating them recovers a physically consistent, non-linear law and reduces trajectory error sevenfold relative to conventional modelling. End-to-end differentiability further enables gradient-based optimisation of high-dimensional operating profiles, identifying a 300-variable temperature profile that reduces crystal aspect ratio by 25% at constant mass, facilitating superior product attributes. Our framework augments, rather than replaces, mechanistic modelling, offering a general route to learning transport models from data and optimising processes with them.
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All authors Arthur Jessop, Mohammed Alsubeihi, Ben Moseley, Ashwin Kumar Rajagopalan

Computational Engineering

sp-DBA: a general framework for adaptive transform-domain computation

Jingkun Jiang, Pingchuan Deng and Yang Xia

Transform-domain methods simplify analysis and computation, making them central to scientific computing and signal processing. However, existing adaptive strategies often introduce new data structures or require substantial workflow redesign, limiting efficient execution on massively parallel hardware. Here we present spectral dynamic block activation (sp-DBA), an adaptive acceleration framework for transform-domain workflows. sp-DBA dynamically activates transform-domain computation blocks as a calculation evolves, concentrating computation where it is needed while retaining numerical accuracy and optimized transform operations. Across representative workflows in materials science, biology, and optical communications, the demonstrated implementations achieve speedups of up to 28.1-fold for transform-domain updates and up to 8.4-fold overall acceleration; strong- and weak-scaling tests on up to eight GPUs show that the local adaptive update remains effective within distributed FFT workflows. By introducing execution-level adaptivity into mature transform-domain workflows without rebuilding existing solvers, sp-DBA extends the scale, duration, and complexity of scientific simulations and signal-processing calculations on modern parallel systems.
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Computational Engineering

What Do CAE Simulation Agents Really Need Beyond a Generic Harness?

Jiasheng Shi and Tianhan Zhang

Computer-aided engineering (CAE) simulation is among the largest and most demanding areas of engineering, where setting up a solver such as OpenFOAM, FEniCS, or COMSOL takes real expertise. Large language model (LLM) agents promise to turn a natural-language request into a working simulation, and recent CAE agents add simulation-specific machinery: multi-agent decomposition, domain retrieval, and scripted reflection. That machinery suited weak base models; modern harnesses already supply multi-turn reasoning, tool use, and execution feedback. We ask what a CAE simulation agent still needs beyond a generic harness. With information access and repair budget held fixed, a single-agent harness matches or beats multi-agent specialized systems (FoamBench 96.4% vs.\ 88.2%). Ablations trace this to capabilities the harness already provides: execution-feedback repair lifts FoamBench from 71.8% with no repair round to 96.4%, while scripted reflection adds nothing. The one input that still helps is domain knowledge supplied as solver tutorials, our largest measured gain (80.9% to 96.4%).
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Computational Engineering · Cross-listed from cs.CL

Enhancing Financial Question Answering: A Novel Benchmark Dataset of Banks' financial statements

Arianna Miola, Bruno Spaccavento, Lorenzo Silotto et al. (+2 more)

The comparative analysis of banks' financial statements poses significant challenges for automated question answering systems due to their complexity, substantial length, technical language, and inhomogeneity of both textual and numerical content across different jurisdictions and institutions. We introduce FinRAG-QA, a novel benchmark dataset for financial question answering, which comprises 999 practitioner-curated questions on 10 standardised indicators, grounded in 209 annual and Pillar 3 reports from 24 major European and U.S. banks spanning 2019-2023. Unlike prior financial QA benchmarks, which centre on U.S. filings and single-institution analysis, FinRAG-QA targets cross-institutional retrieval over documents averaging 198k words, longer than any existing financial QA resource. On this benchmark we evaluate a multi-stage RAG pipeline and isolate the contribution of each component. Contextual chunk enrichment combined with a retrieval-optimised embedding model raises NDCG@10 from 0.322 to 0.710; conditional on the ground truth being retrieved, a reasoning-optimised generator raises answer accuracy from 44.6% to 79.0% (+34.4 percentage points), at roughly 20x the generation latency. We further show that cross-encoder reranking degrades retrieval when the first-stage ranking is already strong, and that a single top-ranked chunk outperforms larger contexts at generation time. Experiments were run in late 2024-early 2025 with the models available at that time.
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All authors Arianna Miola, Bruno Spaccavento, Lorenzo Silotto, Marco Bianchetti, Luca Cagliero

Computational Engineering

Calibration of neural viscoelastic models via full-field data

Brain M. Riemer, Markus Kästner and Karl A. Kalina

We propose an unsupervised learning framework for calibrating a physics-augmented neural network (PANN) for small-strain viscoelasticity via full-field data. It only requires quantities that are directly accessible in real experiments for training, namely global reaction forces and surface displacements. The underlying PANN is embedded in the generalized standard materials theory, in which two scalar-valued potentials render the constitutive model thermodynamically consistent by construction, while invariant-based representations of the free energy and the dual dissipation potential additionally ensure material symmetry. Considering a thin specimen under the plane stress assumption, we formulate a constrained optimization problem based on the equilibrium gap method in combination with quasi-Newton optimizers and automatic differentiation. Thereby, the unknown out-of-plane strain follows from the plane stress condition and the evolution of the internal variables is captured by an implicit time integration scheme. The resulting system of nonlinear equations is solved via a local Newton iteration at quadrature point and time step. To drastically reduce the computational cost of training, the backward adjoint method is employed to compute the gradient of the target loss, instead of backpropagating through all Newton iteration steps. The proposed framework is demonstrated for synthetic data, including noisy displacements and forces, showing excellent agreement across a wide range of deformation rates and load paths.
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Computational Engineering

Markovian Shock-Source Tracing and Multidimensional Asset Roles in Exchange Rates, Gold Futures, and Bitcoin

Seung Ho Choi, Seoin Jang, Hyunwoo Lee et al. (+1 more)

This study examines cross-asset connectedness in an international financial network of major exchange rates, gold futures, and Bitcoin. Moving beyond the conventional net transmitter–receiver classification, we characterize asset roles through three complementary dimensions: direct spillover transmission, stationary source-tracing dynamics, and multistep upstream connectivity. Return spillovers are estimated using VAR generalized forecast-error variance decomposition (VAR-GFEVD). The positive pairwise net-spillover structure is then mapped into a row-stochastic Markov kernel whose transitions trace dominant net-spillover sources in the reverse direction of the original transmission edges. A stationary departure flux describes long-run movement in this source-tracing chain, while Viral Centrality is evaluated by the deterministic probability-propagation algorithm of Fink et al. to approximate multistep upstream reach. Empirically, gold futures emerge as the dominant direct net transmitter and the leading stationary source-tracing node, but do not have the largest Viral Centrality. Several exchange-rate nodes classified as direct net receivers have relatively high Viral Centrality, indicating broad conditional access to upstream source nodes. Bitcoin occupies an intermediate role. An auxiliary specification including the U.S. Dollar Index yields qualitatively similar role differentiation. The results show that direct connectedness, stationary source tracing, and approximate multistep upstream reach cannot be inferred from NET spillovers alone. All diffusion interpretations are descriptive and conditional on the estimated network, row normalization, and restart closure.
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All authors Seung Ho Choi, Seoin Jang, Hyunwoo Lee, Hayoung Choi