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Computer Science Computational Engineering

cs.CE Watch Computational Engineering

Thursday 24 September

Computational Engineering

Perspectives on Sustainable Computational Science and Engineering

Julia Kowalski, Ann S. Almgren, Achim Basermann et al. (+43 more)

Computational Science and Engineering (CSE) combines expertise at the intersection of engineering, applied mathematics, and computer science to form powerful methods for model-based design and model-based decision support across disciplines. Today, CSE methods and tools have an impact as an enabling technology in the development of increasingly sustainable products, processes, and operations. However, sustainability is rarely considered holistically in the context of CSE itself. This paper develops a perspective on sustainability in CSE by distinguishing CSE as an enabler of sustainability in application domains from sustainability within CSE itself, which rests on two complementary pillars: sustainable computing and sustainable software. We present illustrative examples for these pillars and derive recommendations and best practices for CSE stakeholders. Developing an understanding of how sustainability can create added value in CSE is an important future direction for the field. This calls for a concerted effort within the CSE community to define measurable outcomes and best practices that embed sustainability as a core design principle rather than an afterthought.
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All authors Julia Kowalski, Ann S. Almgren, Achim Basermann, Alan Correa, Immo Huismann, Uwe Naumann, Hendrik Ranocha, Matthias Rauter, Jens Saak, Michael Schlottke-Lakemper, Robert Speck, Manuel Torrilhon, Mario Wolter, Niklas von der Assen, Maria Guadalupe Barrios Sazo, Dipankul Bhattacharya, Marcel Bock, Faras Brumand-Poor, Dirk Brömmel, Semih Burak, Sara Grundel, Christiane Helzel, Bruce Hendrickson, Robert Klöfkorn, Katharina Kormann, Zahra Lakdawala, Hannah Lanzrath, Camilla Lüttgens, Anna Matuszy`nska, Matthias Meinke, Abigail Morrison, William S. Moses, Georgii Oblapenko, Marcel Pfeiffer, Jana Sasse, Katharina Schmitz, Miriam Schulte, Anna Schwarz, Pit Steinbach, Ingo Steldermann, Anton Stratmann, Lambert Theisen, Joseph De Veaugh-Geiss, Tim Wegmann, Gabriel Wittum, Anil Yildiz

Computational Engineering

Voxel-based block variational quantum linear solver: a hybrid quantum-classical method for static analysis of solids

Feng Wu, Chen Li, Li Zhu et al. (+2 more)

In solid mechanics, finite element discretization of large-scale static problems produces large sparse linear systems whose solution requires substantial computation time and memory. The variational quantum linear solver (VQLS) offers a hybrid quantum-classical route, but its use in quantum finite element analysis is limited by the decomposition of nonunitary matrices, barren plateaus, and the measurement cost of expectation values. We propose a voxel-based block variational quantum linear solver (Voxel-BVQLS) that combines structured matrix decomposition, the principle of minimum potential energy, and batched quantum tests. First, we construct an LCU decomposition of the stiffness matrix from the recursive block-banded structure of voxel-grid finite element matrices, with the number of unitary terms bounded independently of the problem size. Second, we optimize the ansatz parameters using a minimum-potential-energy objective in place of a conventional VQLS loss function, thereby mitigating barren plateaus in the studied problems. Third, we introduce a block-Hadamard test whose circuit directly estimates weighted sums of multiple inner products, reducing the number of circuit configurations required per iteration. We assessed the proposed method in noiseless classical simulations using three examples. These examples show that the method reduces both the number of unitary terms in the LCU decomposition and the number of iterations required to converge, while still yielding solutions of finite accuracy. Voxel-BVQLS thus provides a structured hybrid quantum-classical framework for quantum finite element analysis on regular grids.
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All authors Feng Wu, Chen Li, Li Zhu, Yuxiang Yang, Xu Guo

Computational Engineering · Cross-listed from cs.LG

Beyond Compression: Training Latent Representations for Stable Long-Horizon Rollout in Neural Surrogate Solvers

Andreas E. Robertson, Ashley T. Lenau, John D. Shimanek et al. (+5 more)

Latent neural surrogate solvers, or latent dynamics models, accelerate simulations of time-dependent physical systems by evolving a compressed latent space rather than resolving full-resolution fields directly. In principle this reduces computational cost and simplifies learning, but in practice errors often accumulate rapidly during long autoregressive rollouts, limiting predictive utility. We show that this instability does not stem from the latent representation itself, but arises when it is trained solely for reconstruction, producing representations poorly suited to long-horizon forecasting. We systematically evaluate training-level interventions that align latent representations with long-horizon rollout: Koopman operator learning and Hamming noise injection during autoencoder training to improve compression, together with noise injection and multi-step rollout fine-tuning to improve dynamics. Interventions that improve long-horizon rollout stability often degrade conventional training metrics, including reconstruction and one-step prediction accuracy. Collectively, these interventions reduce long-rollout error by approximately 40% and match or exceed the accuracy of full-resolution models on two physics benchmarks, while requiring 2 orders of magnitude fewer floating point operations and half the GPU memory. Applied to mesoscale crystal-plasticity simulations of high-cycle fatigue, the resulting surrogate achieves stable extrapolation over horizons orders of magnitude beyond those observed during training. More broadly, these results show that neural compression should be designed not merely to reduce dimensionality, but to restructure the solution space for stable dynamical evolution, a key requirement for reliable, efficient neural surrogates in scientific applications.
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All authors Andreas E. Robertson, Ashley T. Lenau, John D. Shimanek, Benjamin A. Jasperson, Vivek Oommen, David L. Damm, Krishna Garikipati, Remi Dingreville

Computational Engineering

Adapting a Large Language Model Crash-Severity Pipeline to Tennessee: Performance Across Sampling Strategies

Abhilasha Saroj, Pranav Govindu, Bharat Sharma et al. (+1 more)

State crash databases differ in structure, coding, and injury-severity distributions, limiting direct reuse of predictive workflows across jurisdictions. This study adapts the SafeTraffic Copilot large language model (LLM) crash-severity workflow to a three-year Tennessee inventory of 624,392 crashes. Tennessee crash, roadway, vehicle, and person attributes were harmonized and converted into textual prompts while unavailable values were preserved rather than inferred. Llama 3.1 8B was fine-tuned using low-rank adaptation to classify five injury-severity categories. Random, county-based, and severity-balanced sampling strategies were evaluated using separate in-sample and unseen-test experiments; unseen-test experiments used 70/15/15 training, validation, and test splits. On their respective unseen test sets, random and county-based models achieved weighted F1-scores near 80%, but macro F1 remained below 47% and fatal-crash F1 below 27%, showing that strong aggregate performance can mask weak recognition of rare outcomes. Within its balanced evaluation population, the severity-balanced model achieved weighted and macro F1-scores of 57.7% and a fatalcrash F1-score of 65.9%, yielding more even class-level performance. Because each sampling strategy used a different test subset, crossstrategy differences are descriptive rather than controlled rankings. The results highlight the importance of interpreting LLM crashseverity performance together with sampling design, class balance, class-level metrics, and evaluation-population composition.
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All authors Abhilasha Saroj, Pranav Govindu, Bharat Sharma, Usman Ahmed

Computational Engineering · Cross-listed from cs.CL

TimeBraid: Unifying Time Series and Language for Understanding and Forecasting

Xinyue Wang, Jiacheng Pang, Kun Zhou et al. (+7 more)

We present TimeBraid, a series of unified time-series and language models that align pretrained language models and pretrained time-series foundation models through interleaved global residual attention layers. Each model inherits knowledge, instruction following, and reasoning from one side, continuous-signal perception and zero-shot forecasting from the other, and fuses the two in a shared representation space where both modalities are understood and generated. We study the design choices that make such unified modeling work: where to align the two representation spaces, how to ground language in temporal structure, how to balance understanding with generation, and how to keep joint optimization stable. The resulting recipe combines a unified prompting scheme for diverse time-series and text tasks, stabilized joint training, and supervision from 2.2M curated series–text pairs and 4.9M instruction-tuning samples. Across benchmarks spanning time-series perception, understanding, reasoning, and both context-aided and unimodal forecasting, TimeBraid remains competitive with far larger general-purpose models and task-specific counterparts.
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All authors Xinyue Wang, Jiacheng Pang, Kun Zhou, Kexin Zhang, Defu Cao, Fan Feng, Faisal, Songyao Jin, Yan Liu, Biwei Huang

Computational Engineering

Dynamic production control and deadlock prevention in conveyor equipped manufacturing systems

Claudio Castiglione, Erica Pastore and Arianna Alfieri

Flexible CONWIP approaches enable the dynamic production control to align production performance with production targets in the presence of high-fluctuating demand and system variability. However, frequently changing the WIP level might generate nervousness and, thus, can be practically infeasible. This paper investigates the combined use of job sequencing and routing to enable dynamic control in a CONWIP system comprising six unreliable workstations interconnected by conveyor carousels. Job sequencing and routing have been investigated using scenario analysis and discrete-event simulation. The results shed light on the effectiveness of the proposed approach, which improves throughput and enables dynamic production control without changing the WIP level. The results also provide managerial insights for system design, considering the impacts of the job-handling system (transport) and the minimum digital and technological requirements from Industry 4.0 for implementing the proposed approach.
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Computational Engineering

AFT Neural Function Approximators for 1D Nonlinear Force Laws

Miriam Goldack, Johann Groß, Malte Krack et al. (+1 more)

Nonlinear contacts and friction strongly influence the vibration response of assembled structures, but their accurate numerical treatment is computationally demanding. The harmonic balance method is widely used to compute periodic steady-state responses, yet the required alternating frequency-time scheme becomes costly for nonsmooth and hysteretic nonlinearities and must be repeated throughout the nonlinear solution process. Here we show that this procedure can be replaced by neural networks that directly map displacement Fourier coefficients to nonlinear force coefficients and provide the corresponding Jacobian through automatic differentiation. The surrounding solver and continuation algorithms remain unchanged for the computation of frequency response curves. The neural networks exclusively learn individual nonlinear elements rather than complete system responses. Physics-based nondimensionalization and phase normalization facilitate the learning process and enable a single trained network to cover a wide range of parameter combinations. Building on the cubic spring, unilateral spring, and Jenkins elements considered here, the approach points toward a reusable library of nonlinear-element surrogates that can be combined in arbitrary number and location within a mechanical system. By bypassing the iterative force evaluation in time domain, the method offers favorable computational scaling for high-resolution analyses and systems with many nonlinear elements.
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All authors Miriam Goldack, Johann Groß, Malte Krack, Merten Stender

Computational Engineering · Cross-listed from cs.AI

Claim-Gated Source-Risk Auditing for Generative Search

Kainan Zhou, Chuhong Xu, Gangzhen Qian et al. (+1 more)

A generative search answer can cite a supported passage yet omit a source relationship that changes its interpretation. We specify a claim-gated audit of the query-source-answer tuple. An omission is resolved only when relationship evidence, answer adoption, materiality, and disclosure are all observed; incomplete evidence remains unresolved rather than being treated as independence. The specification separates this endpoint from citation support and review priority, and binds decisions to versioned evidence spans. A reference checker makes the record contract executable. On an exhaustive synthetic suite, it reproduces all 81 three-state predicate combinations and rejects 192 deliberately malformed records. Common-guard baselines and predicate ablations isolate endpoint logic from missing-evidence handling, while controlled transitions check support separation and evidence removal. These are finite contract-conformance results, not detector accuracy or evidence of improved user outcomes. We define the independent annotation, held-out evaluation, and paired utility tests still required to establish semantic validity and deployment benefit.
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All authors Kainan Zhou, Chuhong Xu, Gangzhen Qian, Zhaoyi Li

Computational Engineering · Cross-listed from cs.AI

AlphaDiverse: Post-Training Local Quantitative Research Agents for Diverse Exploration in Alpha Factor Mining

Qingzhuo Wang, Zikun Wei, Zhihua Wei et al. (+1 more)

Large language model (LLM)-based multi-agent systems can automate alpha factor mining, but their reliance on external APIs limits control over cost, availability, and confidentiality. Long research loops also tend to revisit a few successful economic mechanisms that lead to research path collapse. To address these limitations, we propose AlphaDiverse, a framework that integrates a multi-agent alpha research system, diverse research path collection, and post-training for local agents. We let the research system generate complementary plan portfolios and vary research environments across loops to collect diverse research paths. Using these diverse traces, we warm-start local Planner and Realizer agents with supervised fine-tuning. Then, we propose a joint GRPO method to optimize both of them using predictive quality and diversity of contributions. Research feedback is confined to inner period data, while a frozen final model is evaluated on a later outer period data, thereby avoiding test-set tuning. Experiments across four Chinese stock universes show that AlphaDiverse can combine competitive prediction with broader exploration.
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All authors Qingzhuo Wang, Zikun Wei, Zhihua Wei, Wen Shen

Wednesday 23 September

Computational Engineering

Compositional Embedding Architecture for Physical Field Prediction in Componentized Aerospace Systems

Qineng Wang, Xinrui Zhou, Shuwen Yue et al. (+3 more)

Spacecraft thermal design requires repeated evaluation of how variations in the number and spatial arrangement of heat-generating components and in thermal boundary conditions affect the temperature field. High-fidelity numerical simulations are computationally expensive and therefore difficult to use for large-scale design screening. Although surrogate models can accelerate temperature-field prediction, existing approaches generally encode each complete configuration as a whole and do not explicitly exploit the reusability of local physical constituents across configurations, which limits their accuracy for component counts and combinations not covered during training. To address this issue, we propose the Tree-Structured Factor Composition Network (TFCN), which decomposes complex spacecraft thermal configurations into reusable local physical factors and employs a tree-structured composition module to learn the global temperature-field response associated with different factor combinations. TFCN is evaluated on two-dimensional steady-state spacecraft thermal-analysis cases with prescribed-temperature and radiative-flux boundary conditions. The model is trained exclusively on configurations containing no more than 15 heat-generating components and evaluated on unseen configurations containing 16-25 components. For the prescribed-temperature and radiative-flux cases, TFCN achieves component-count out-of-distribution RMSE values of 4.21 K and 18.62 K, respectively, representing reductions of 65.6% and 33.6% relative to the strongest baseline. These results demonstrate that TFCN improves the reliability of temperature-field prediction under variations in component count and provides an efficient surrogate for rapid spacecraft thermal-design evaluation and large-scale configuration screening.
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All authors Qineng Wang, Xinrui Zhou, Shuwen Yue, Kangli Bao, Hairun Xie, Yonghe Zhang

Computational Engineering · Cross-listed from cs.AI

PAWS: Policy-driven Agentic World Simulation

Tiviatis Sim, Jia Hui Woon, Xinming Gao et al. (+5 more)

Policy interventions propagate through public communication, institutional decisions, and stakeholder responses, yet datasets for financial multi-agent simulation rarely connect these processes to temporally aligned historical evidence. We introduce PAWS, a Policy-driven Agentic World Simulation dataset covering 36 verified U.S. financial and economic policy episodes, 12,727 policy-linked news records, and 65,291 source-grounded stakeholder actions. Each action is linked to its supporting news and represented by a multi-layer event frame capturing its interaction mode, financial-action family and subtype, semantic attributes, and conditional mappings to external taxonomies. Entities are resolved to normalized organizations, and actions are aligned with daily market-return context to support policy-agent simulation replay. On 2,522 stratified action samples, independent AI and human reviewers achieved 89.4% initial agreement on interaction mode, with disagreements subsequently adjudicated. Case studies of the 2008 short-selling ban and 2001 decimalization recover documented policy timelines and associated market patterns across both dense and sparse news settings. A replay study further shows that high accuracy can mask failure to detect rare stakeholder actions, identifying action timing and calibration as central challenges. PAWS provides an auditable substrate for evaluating agent influence, policy-response cascades, and action-outcome alignment in historically grounded financial simulations.
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All authors Tiviatis Sim, Jia Hui Woon, Xinming Gao, Chen Gao, Fengbin Zhu, Zheng Huanhuan, Chua Tat Seng, Kenji Kawaguchi

Computational Engineering · Cross-listed from cs.CL

Can LLMs Catch a Rigged Backtest? A Clean-Control Calibration Benchmark

Makar Ulesov, Vladislav Smirnov, Omar Ibrahim et al. (+1 more)

Backtest auditing is a calibration problem: high flaw recall is not useful when the model falsely flags matched clean strategies. We build a 96-item paired benchmark in which every flawed backtest has a clean control that holds strategy, dates, code style, labels, and reporting scaffold fixed while changing one methodology detail. A deterministic scorer separates flaw recall, clean-control false positives, evidence localization, and fix relevance. Over 1440 cached audits from four text endpoints, the primary DeepSeek auditor reaches 100.0% closed and clean-aware code recall, but open prompts over-flag 93.8% of clean code controls, and clean-aware all-three specificity is 87.5% even where recall saturates. A clean-aware warning drops DeepSeek code false positives from 20.8% (95% CI 11.7–34.3) to 0.0% (0.0–7.4) at unchanged recall, while the budget anchor still flags 38/48 clean controls under the same prompt. Reporting recall alone would rank three of these four models identically; reporting the clean-control rate separates them by 79 points.
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All authors Makar Ulesov, Vladislav Smirnov, Omar Ibrahim, Arsenii Bobovnikov

Computational Engineering

Topology optimization of multimaterial aircraft pylons using generalized shape function approach

Swagatam Islam Sarkar and Prabhat Kumar

As the primary structural component connecting the engine to the wing or fuselage, an aircraft pylon requires optimized structural efficiency; this paper provides topology optimization of multimaterial pylons using the generalized shape function (gSF) approach. The gSF method uses $n$ natural-coordinate design variables per element to provide optimized designs up to $2^n$ distinct material phases while promoting close to discrete material layouts in conjunction with the density and formulated Heaviside projection filters. Pylon structural compliance is minimized subject to volume constraints. Exploiting the geometric features of a typical pylon structure, multimaterial evolution is performed on a corresponding 2D design domain representing the midplane, with up to 14 candidate materials. The optimized two-dimensional layout is then extruded to achieve the corresponding three-dimensional optimized pylon structure. The Method of Moving Asymptotes is employed to achieve the final design variables. The resulting convergence histories exhibit smooth and stable objective minimization. The results highlight the capability of the multimaterial topology optimization framework to effectively optimized aircraft pylons with multiple candidate materials, without requiring a considerable expansion of the design variable set.
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Computational Engineering · Cross-listed from cs.AI

SHRAV: State-Hypothesis-Reason-Action-Verify Framework for Physical Modeling and Inverse Design

Ziheng Guo and Yang Bu

Physical modeling and inverse design require computation that can continue from reusable state. We introduce SHRAV, an architecture-independent computational framework organized around State, Hypothesis, Reason, Action, and Verify. Its central mechanism is a state-continuation core with declared reuse boundaries and explicit roles for learned evolution and numerical quantities. Forward configurations evolve predictive state and read out physical responses; inverse-design configurations additionally generate target-directed modifications and consume evaluator feedback. Electromagnetic world-model studies are mapped to forward configurations, with selected readout and reuse diagnostics reported here. Computational lithography demonstrates an inverse-design configuration: four fixed-weight design updates improve thresholded aerial-image intersection-over-union from 0.5313 to 0.8153 under independent scalar-pupil replay, with a maximum absolute IoU difference of approximately 0.000824 between predictor estimates and independent replay.
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Computational Engineering

Geometry-Based Metrics for Early-Stage Hull-Form Producibility Screening

Andrea Serani and Kevin Maki

This paper presents a representation-aware framework for geometry-based screening of hull-form producibility at early design stages. The proposed signature combines dimensionless total and signed developability deviation with curvature-class area fractions, distributed fields, metric-specific validity, and representation provenance. These descriptors characterize surface features relevant to plate forming and developability, but are not calibrated predictors of fabrication cost, forming effort, or process feasibility. Native IGES/STEP boundary representations (BReps) are evaluated through direct differential geometry and trimmed-domain quadrature, whereas triangulated surfaces use discrete curvature recovery and area-weighted aggregation. Analytical and semi-analytical controls verify the formulation, while matched-face BRep-to-mesh tests assess discrete curvature recovery. Application to DTMB 5415, KCS, JBC, and KVLCC2M shows that curvature intensity and areal extent provide complementary information and that derivative-based outcomes can be representation sensitive. KCS, for example, exhibits approximately 24% greater developability deviation than DTMB 5415, while double-curved regions occupy 72.9% of its valid surface versus nearly the entire DTMB valid surface. The resulting quantities provide an early geometric screening layer for subsequent use as objectives, constraints, surrogate responses, or design-space features. HullProd, the companion open-source software, implements the signature, distributed fields, validity, and provenance.
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Computational Engineering

Parallel Multi-Fidelity Expected Improvement Method for Efficient Global Optimization

Zhendong Guo, Qineng Wang, Liming Song et al. (+1 more)

Multi-Fidelity optimization (MFO) has received extensive attentions in engineering design, which resorts to augmenting the small number of expensive high-fidelity (HF) samples by a large number of low-fidelity (LF) but cheap samples to improve the optimization performance. A key factor that influences the effectiveness of MFO is how to adaptively assign samples for HF and LF simulations in the iteration process. To address such sample assignment issue in MFO, we propose a new infill criterion named as Filter-GEI, which imposes an adaptive filter function on top of the generalized expected improvement (GEI) acquisition function. In particular, by taking the correlations between HF and LF models into account, the Filter-GEI can efficiently allocate HF and LF samples to achieve a good balance in between the local and global search. Furthermore, considering parallel computing, the Filter-GEI infills multiple HF and LF samples in each iteration, which can further improve its efficiency as computing power increases. Through tests on five mathematical toy problems and one engineering problem for the turbine blade design, the effectiveness of the proposed algorithm has been well demonstrated.
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All authors Zhendong Guo, Qineng Wang, Liming Song, Jun Li

Computational Engineering

KATOsuper: Surrogate-accelerated neural topology optimization with sensitivity-consistent Fourier neural operators

Shengyu Yan and Jasmin Jelovica

Topology optimization (TO) remains computationally intensive due to repeated finite element analysis (FEA) evaluations required at each iteration. While neural network-based surrogates offer potential acceleration, existing approaches often suffer from gradient inconsistency between predicted objectives and sensitivities, leading to optimization instability. This work presents KATOsuper, an objective-agnostic framework that couples neural-reparameterized topology optimization with a Sensitivity-Consistent Fourier Neural Operator (SC-FNO). The framework employs the forward_split architecture, which derives deployed sensitivities via automatic differentiation through the predicted objective field and thereby preserves consistency between the predicted objective and the gradient used for optimization. The case studies include three 2D benchmark problems and three 3D structures considering compliance or stress minimization. A physics-informed multi-channel input encoding with Fourier position embedding enables resolution-invariant learning, supporting zero-shot extrapolation beyond the training resolution, with useful performance at moderate scaling factors and topology-preserving exploration at up to 64x without retraining. The framework extends to 3D through KATO3D, featuring novel KANConv3D blocks with learnable B-spline activations. KATOsuper demonstrates 15–110x deployment-time speedup over MATLAB baselines while maintaining competitive optimality, with the clearest gains observed in complex 3D and stress-optimization cases. The insight that sensitivity direction matters more than magnitude enables robust optimization even with approximate physics evaluation, extensible to other differentiable physics-driven design objectives.
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Tuesday 22 September

Computational Engineering · Cross-listed from q-fin.CP

Loss Choice or Model Choice? The Role of Forecast Level in Cryptocurrency Volatility Forecasting

Andrzej Tokajuk and Jarosław A. Chudziak

Volatility forecasts play a central role in financial risk management because their overall level and day-to-day movements affect downstream decisions. Most studies compare forecasting models while keeping the training loss fixed. Yet losses emphasise different errors and can target different properties of future volatility, so raw comparisons may combine persistent forecast-level differences with differences in daily forecast movements. This leaves unresolved whether the importance of loss choice comes mainly from the forecast level it targets or from differences that remain after level adjustment. We address this gap through a comparison of seven losses and five models across major cryptocurrencies. Validation-based alignment adjusts the forecast level before the raw and aligned forecasts are evaluated using statistical scores and one-day Value-at-Risk. Before alignment, marginal score variation is greater across losses. After alignment, model choice becomes the larger source of variation in the full five-model comparison, while cross-loss differences in VaR breach rates narrow substantially. Our contribution is a comprehensive evaluation of loss and model choice that shows why losses can appear so influential in raw comparisons and how this interpretation changes when forecast level and downstream risk are considered explicitly.
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Computational Engineering · Cross-listed from cs.LG

DeepFEAv2: Deep Learning for Transient Finite Element Analysis Beyond Structured Meshes

Georgios Triantafyllou, Panagiotis G. Kalozoumis and Dimitris K. Iakovidis

Finite Element Analysis (FEA) is widely used for transient mechanical simulations, but its high computational cost limits real-time and high-resolution applications. Deep learning surrogate models can reduce this cost; however, many existing approaches are restricted to steady-state prediction or cannot jointly predict Node- and Element-based Outputs (NEO) over time. The state-of-the-art DeepFEA framework has addressed these issues but remains limited to structured finite element (FE) meshes. To overcome this limitation, this study proposes DeepFEAv2, a deep learning surrogate framework that enables prediction of transient FEA simulations across different FE mesh topologies and element types. The main contributions of DeepFEAv2 are: (a) a module that uses the FE connectivity matrix to organize input features by element and arrange them into an input sequence guided by the mesh topology; (b) a novel neural network architecture designed to process the input sequence and jointly predict NEO over time; and (c) a FEA-informed optimization strategy for regularizing these NEO predictions. DeepFEAv2 was evaluated on structured and unstructured 3D linear elastic datasets, as well as on a pressure-driven aortic valve dataset. DeepFEAv2 achieved R^2 values up to 0.99 and normalized errors as low as 0.38%. Compared with DeepFEA, it achieved up to 38.0% relative increase in R^2 and up to 87.1% reduction in normalized error. DeepFEAv2 also performed inference up to three orders of magnitude faster than traditional FEA. These results demonstrate that DeepFEAv2 can efficiently model transient FEA simulations across increasingly complex FE settings, providing a scalable surrogate framework for transient FEA.
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Computational Engineering

Multimaterial Topology Optimization using SIMP, DMO, and gSF: A comparative study with Honeycomb tessellations

Bhargav kumar Duru and Prabhat Kumar

This paper presents a comparative study of multimaterial topology optimization (MMTO) using the extended SIMP, Discrete Ma- terial Optimization (DMO), and generalized shape functions (gSF) ap- proaches. The design domain is parametrized using hexagonal elements. Each element has six neighboring elements, thereby improving element connectivity and yielding a relatively uniform local mesh structure. This characteristic reduces mesh-related effects on the optimized designs com- pared with conventional triangular and quadrilateral discretizations. Struc- tural compliance is minimized at the prescribed volume fractions. The optimization is performed using the method of moving asymptotes. The resulting optimized topologies are compared in terms of material distri- bution, structural performance, convergence behavior, and the character- istics of the obtained material interfaces. The comparative investigation provides insights into the working principles and performance of the ex- tended SIMP, DMO, and gSF interpolation schemes for MMTO.
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Computational Engineering

Analysis of trade-offs in urban heat mitigation using a Bayesian Optimization framework for an urban canopy layer model

Rebekka Walter, Johanna Gelhaus, David Anton et al. (+2 more)

To mitigate the challenges of climate change and intensifying heat stress in urban areas, local adaptation strategies are discussed and introduced in cities worldwide. To understand processes and potential trade-offs of these strategies a Bayesian optimization and surrogate modeling framework was employed to investigate urban parameter ranges of heat mitigation strategies with focus on three thermal metrics: daytime air temperature, Universal Thermal Climate Index (UTCI), and nighttime air temperature. Based on an urban street canyon configuration, it was shown that heat mitigation measures that reduce daytime air temperature and UTCI are often associated with higher nighttime temperatures. This results in a curved Pareto front that reflects the trade-off between daytime and nighttime thermal comfort. Under identical forcing conditions, different urban configurations, varying in geometry, vegetation, and surface characteristics, are shown to alter peak canyon air temperature by up to 5.2~$^\circ$C during the day and 2.6~$^\circ$C at night, while UTCI varies by up to 7.9~$^\circ$C, demonstrating that favorable urban configurations can substantially mitigate microclimatic heat stress. These findings suggest that combining multi-objective Bayesian optimization and surrogate modeling can help bridge the gap between computationally intensive climate simulations and practical decision-making in urban planning. An interactive visualization tool was developed to explore these trade-offs, making the often opposing relationships between urban parameters and the three thermal metrics directly accessible to planners.
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All authors Rebekka Walter, Johanna Gelhaus, David Anton, Henning Wessles, Stephan Weber

Computational Engineering

Scientific capabilities and deployment sustainability of small-scale LLMs in biological wastewater treatment

Run-Ze Xu, Chu-Kuan Jiang, Dylan Ming-Han Li et al. (+3 more)

Large language models (LLMs) are emerging as scientific assistants, yet their computational demands and limited domain specialization constrain sustainable deployment in environmental engineering. Here, we investigate whether domain-specialized small-scale LLMs can combine scientific capability with sustainable deployment in biological wastewater treatment. We developed a benchmark evaluating three scientific capabilities of LLMs: retrospective cognition, comprehension fidelity, and prospective extrapolation. BioWater (8 billion parameters, fine-tuned on specialized domain knowledge) achieved higher comprehension-fidelity scores than participating human experts and performance comparable to a 397-billion-parameter general-purpose LLM in retrospective cognition and prospective extrapolation. Human-BioWater collaboration generated a scientific hypothesis that was subsequently supported by laboratory experiments, demonstrating its potential to contribute to prospective scientific research. We further evaluated the economic and environmental implications of LLM deployment across global wastewater treatment plants (WWTPs). Locally deployed small-scale LLMs became more sustainable than cloud-based large-scale LLMs as inference demand increased in intelligent WWTPs. These findings highlight domain-specialized small-scale LLMs as a promising pathway towards scientifically capable, computationally efficient, and sustainably deployable artificial intelligence for wastewater treatment.
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All authors Run-Ze Xu, Chu-Kuan Jiang, Dylan Ming-Han Li, Hong-Xiao Guo, Jia-Shun Cao, Guang-Hao Chen

Computational Engineering · Cross-listed from cs.LG

Targeted Review for AI-Assisted Biodiversity Surveys: Active Continuous-Score Occupancy Modeling

Timm Haucke, Lauren Harrell, Justin Kay et al. (+2 more)

We increasingly use machine learning to label scientific datasets. The models we develop and deploy are improving all the time, but they are not and will likely never be perfect. Mistakes matter, as errors can propagate into our scientific understanding, particularly when systematically biased. Very reasonably, scientists thus review substantial proportions of ML-generated labels to verify or correct mistakes in pursuit of ensuring their scientific findings are not biased by ML. In this work, we focus on helping scientists optimally allocate this reviewing effort relative to their scientific goals. We focus on a specific class of scientists (ecologists) and a specific, widespread, and impactful modeling target (occupancy modeling, which estimates where species are likely to occur, conditioned on environmental factors). We introduce Active Continuous-Score Occupancy Modeling (ACORN), a method that incorporates ML predictions into occupancy models and strategically selects samples for expert review that are maximally informative for downstream ecological analysis. Across camera-trap and bioacoustic datasets, our method recovers ecological conclusions close to those obtained from fully human-labeled data, while requiring substantially fewer expert reviews than non-targeted review policies. Our results suggest that ML-assisted scientific workflows should optimize expert effort for downstream inference, rather than for classifier accuracy alone, especially when human review budget is limited. Our code is available at https://github.com/timmh/acorn
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All authors Timm Haucke, Lauren Harrell, Justin Kay, Mary Clapp, Sara Beery

Computational Engineering

Hierarchical Multi-Task Learning with Liquidity-Aware Signals for Stock Forecasting

Hengyi Yang, Sida Lin, Yiyan Qi et al. (+4 more)

Stock price forecasting is a long-standing challenge in computational finance, driven by the inherent randomness of markets and complex temporal patterns. While recent deep-learning models have raised forecasting accuracy by jointly modeling inter-stock and temporal price dynamics, they conflate inter-stock relationships with intra-stock temporal dependencies and focus solely on the univariate objective of price movement. To address these limitations, we propose LiMT, a Hierarchical Multi-Task Learning framework that integrates liquidity-aware signals for stock price forecasting. LiMT employs a Market Regime Encoder (MRE) module that first extracts contemporaneous cross-stock dependencies, then models each stock's temporal dynamics, yielding a unified latent state. Building on this latent state, we introduce a Liquidity-Driven Learning (LDL) module, a mixture-of-experts architecture that features cross-task gating mechanisms to jointly predict price movement, volatility, and trading volume. We further design an Adaptive Portfolio Optimization (APO) mechanism that converts multi-task forecasts into executable portfolio weights under transaction-cost and liquidity constraints. Extensive experiments on the CSI300 and CSI500 benchmarks show that LiMT performs best among strong neural and tree-based baselines across the reported metrics. In realistic CSI300 backtests, APO improves annualized return from 3.99% to 10.01% and Sharpe ratio from 1.22 to 1.86 over equal weighting, showing that the multi-task forecasts translate into deployable portfolio gains.
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All authors Hengyi Yang, Sida Lin, Yiyan Qi, Yankai Chen, Haohan Zhang, Xianhua Peng, Jian Guo

Monday 21 September

Computational Engineering

Compressible aerodynamics and rigid-body support motion in post-flutter piezoelectric energy harvesting from a pitch-plunge-flap aerofoil

Nikolaos D. Tantaroudas, Ilias Karachalios and Andrew J. McCracken

A piezoelectric transducer on a pitching and plunging aerofoil with a trailing edge flap turns the limit cycle that follows flutter into electrical power, and two modelling changes to that harvester have been studied separately. Replacing incompressible strip theory by the compressible Euler equations reduces the stabilising effect of a transducer mounted on the pitch, where the electrical time constant meets the flutter frequency, and raises the harvested power, and letting the section react against a fuselage free to translate, instead of rigid ground, does the same two things by about the same amounts. Here both changes are put in one model, which needs no new mesh motion in the flow solver, since the support's translation displaces the wall exactly as the plunge does. On the harvested power the two published corrections simply multiply. On the flutter boundary they more than compound, and at strong enough coupling they reverse the sign of the transducer's effect at the load resistance where both companion papers harvest, although neither change alone reverses it. The free support widens the band of resistance over which the two aerodynamic models disagree in sign, carrying its upper edge out by about a factor of three. Above the resistance where the electrical pole meets the flutter frequency, where the transducer acts as an added stiffness, the product of the published corrections predicts the shift to within seven parts in a thousand. Below it, where the transducer acts as a damper whose effect depends on phase, the product is wrong by a factor of up to fourteen, and on the pitch mounting by a sign. The same transducer lowers the boundary while flattening the growth of the instability above it. Whether corrections found one change at a time may be multiplied therefore depends on the quantity and, for the flutter boundary, on the regime of the transducer.
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Computational Engineering

Rigid-body support motion and post-flutter piezoelectric energy harvesting from a pitch-plunge-flap aerofoil

Nikolaos D. Tantaroudas, Ilias Karachalios and Andrew J. McCracken

A piezoelectric transducer on a pitch-plunge aerofoil with a finite-mass trailing-edge flap turns the limit cycle that follows flutter into electrical power. The design findings for such a harvester have so far been obtained with the section's suspension reacting against rigid ground, which is what a wind-tunnel mounting provides. Here a 3-dof aerofoil with a transducer is carried by a fuselage mass that is itself free to translate, so that the coupled system is free-free and the wing's plunge is measured relative to a support that moves. The support degree of freedom is destabilising on its own, softening the flutter mode by taking a share of it in antiphase with the wing's plunge, and it acts on the three transducer mountings unequally, amplifying the plunge mounting's destabilisation, leaving the largest pitch-mounted stabilisation untouched while halving it where the electrical time constant meets the flutter frequency, and leaving the flap mounting alone. The optimum coupling and load resistance do not move, so the matching rules of the rigidly supported section carry over unchanged. The harvested power rises with the support mass at a fixed flow speed for every mounting, and that rise is shown to be an artefact of the fixed-speed convention rather than a property of the cycle. Measured instead at a fixed margin above each system's own flutter boundary, which divides out the boundary's motion, the plunge mounting keeps a real gain while the pitch mounting loses power, so the apparent benefit of a soft support to a pitch-mounted harvester is entirely the boundary coming down to meet the flow speed. A harvester result quoted at a fixed speed and read as a property of the limit cycle is therefore wrong by the whole of that difference.
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Computational Engineering

From functioning to evolving: A complex systems perspective on future self-organised federated energy communities

Abdorasoul Ghasemi

Energy networks face a paradigm shift driven by renewable integration, uncertainty about required flexibility, distributed markets, and smart demand. Increasing asset interactions, cyber dependencies on communication and computation systems, and the deployment of AI agents demand a holistic, system-wide approach to understand the system's emergent behaviour. These transformations affect energy generation, demand adaptation, grid operations, and market dynamics, forming a complex engineered system of systems. We argue that a complex-systems perspective can help to understand this evolution. Drawing lessons from two highly successful large-scale engineered systems, the Internet and agile software engineering, we highlight how prioritising design for evolution over traditional design for functionality enables energy systems to adapt to net-zero dynamics and handle unforeseen uncertainties. We discuss how the notion of federated energy communities (FEC), which aims for an open energy system with distributed coordination, aligns with this perspective. In FECs, each EC produces, stores, and manages its own energy, enabling it to trade with peers, respect grid constraints, and reach fair agreements. Beyond regulatory issues, the main challenge is identifying constraints that can orchestrate EC operations at the scale and reliability standards required of power networks, without unnecessarily limiting the innovation needed for system evolution. We then conclude with tentative design principles for future decentralised, self-organised energy networks through the lens of design for evolution, and how to apply them to a bottom-up architecture of autonomous energy communities.
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Computational Engineering · Cross-listed from stat.ML

Variational objectives for amortized Bayesian inference in inverse problems: The role of posterior conditioning

Abhishek Srivastava, Arijit Hazra and Rajesh Dubbaku

Variational autoencoders (VAEs) offer an efficient approach to amortized Bayesian inference for inverse problems, but posterior accuracy can depend strongly on the choice of variational regularization, particularly when the inverse problem contains weakly identified parameter directions. This study investigates three objectives: a reverse Kullback–Leibler formulation (VAE-KL), an asymmetric Jensen–Shannon formulation (VAE-JS), and a Jensen–Shannon–Wasserstein formulation (VAE-JSWA), which replaces the reverse Kullback–Leibler regularizer with the squared 2-Wasserstein distance while retaining forward-Kullback–Leibler posterior supervision. A full-covariance Gaussian encoder and a pre-trained physics-based surrogate are used for amortized posterior inference. A local linear–Gaussian analysis in the generalized Fisher basis is developed to characterize the variance-dependent gradients of the three objectives. The formulations are first evaluated using linear–Gaussian benchmarks with known posterior solutions and subsequently tested on nonlinear physics-based inverse problems, including an inverse problem governed by a linear ODE and two PDE-constrained problems. VAE-KL performs slightly better than the other formulations in the well-conditioned benchmark, where all three approaches yield comparable posterior approximations, whereas VAE-JSWA provides substantially lower posterior errors in the strongly ill-conditioned benchmark. The nonlinear physics-based problems exhibit a similar conditioning-dependent trend, with JS-based formulations providing greater benefit as posterior ill-conditioning increases. These results indicate that posterior conditioning is an important factor in selecting variational objectives and motivate geometry-adaptive variational inference for Bayesian inverse problems.
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Computational Engineering

Colour me shocked: Exact Molecular Hessians from local MLIPs in O(N) time using sparse differentiation!

Luca Thiede, Andreas Burger and Alán Aspuru-Guzik

The Hessian of the energy with respect to the nuclear positions is indispensable in atomistic modelling. However, constructing this matrix requires $O(N)$ Hessian vector products, traditionally limiting high-accuracy Hessians to small systems. Machine learning interatomic potentials (MLIPs) have accelerated atomistic modelling by providing highly accurate energies and forces at $O(N)$ cost, yet the resulting $O(N^2)$ cost of Hessians remains a practical bottleneck for large systems. Based on the insight that we can derive the sparsity pattern for an MLIP's Hessians in closed form, we show in this paper how to use techniques from sparse automatic differentiation to reduce the cost of a local MLIP's Hessians to a system-size-independent number of Hessian-vector products, yielding overall $O(N)$ total cost without any approximations. We benchmark our approach on a variety of systems ranging from alkane chains to water clusters to $A\beta40$ conformers. Depending on the MLIP configuration, we achieve the linear scaling regime already on relatively small systems, resulting in large runtime reductions between 2$\times$-15$\times$ for these systems. This opens up the possibility of scaling high-accuracy MLIP Hessians to very large systems, such as proteins that were previously inaccessible.
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Computational Engineering

InvestorNerd: An Investment and Financial Insights System Based on User Profiles

John Castillo, Rishika Gautam, Xinyu Wang et al. (+2 more)

InvestorNerd is a web-based platform (investornerd.org) designed to educate and democratize financial understanding by providing accessible, AI-powered investment and personal finance insights tailored to potential user profiles. The system addresses a key challenge in helping everyday individuals, especially those without formal financial education, make sense of investment options and personal financial decisions. The platform offers three interactive tools: Stock Insights - Allows users to input any Stock, Mutual Fund, or Exchange Traded Fund (ETF) ticker to receive a summary of relevant news sentiment and quantitative metrics. Stock Insights Questionnaire - Builds on stock insights by letting users specify a preferred risk tolerance and sector interest. It returns categorized investment tables based on volatility and sector, sortable by dividend yield, return percentages, and others. General Insights Questionnaire - Provides users with personalized insights based on answers to an age-income-expenditure-savings questionnaire. Outputs include possible actions regarding savings strategies, account types (e.g., Roth IRA, UTMA), and potential loan options (e.g., FHA). The output is designed to be clear, unbiased, and educational, empowering novice investors with practical insights. This paper details the design, implementation, and evaluation of InvestorNerd, demonstrating how generative AI and open financial data can be integrated to create scalable, insight-rich tools for financial literacy.
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All authors John Castillo, Rishika Gautam, Xinyu Wang, Harsh Kashyap, Dennis Shasha