Papers

Social networks shape how individuals make decisions and how opportunities are distributed. However, the mechanisms that generate these networks often reflect pre-existing inequalities, and technologies that rely on network-derived signals risk further amplifying such disparities. Algorithmic fairness research largely treats networks as a fixed background, grounding analysis almost exclusively in distributive justice and overlooking how network structures systematically bias decision-making. In this Perspective, we identify ten network effects and trace how they create structural biases in the relationship between what we intend to measure and what we observe. Using academic hiring as an example, we show that network biases are not inherently harmful or beneficial. Determining their legitimacy requires examining the entire decision-making process through the lenses of both distributive and procedural justice while engaging all affected stakeholders. We therefore call for a holistic, networked approach to fairness that moves beyond static group categories and recognizes the dynamic, relational, and structural nature of inequality.

Solving inverse problems with differentiable physics simulators holds the potential to revolutionize scientific discovery and engineering design, as it enjoys both the strict physical correctness from rigorous numerical physics simulators, and the high efficiency and effectiveness from automatic differentiation and gradient-based optimization. However, currently, this paradigm faces performance issues in optimization. In this work, we target benchmarking the performance of different optimizers to solve various inverse problems. We construct 12 differentiable physics simulators spanning physics domains including discrete mechanics, continuous mechanics, atomistic simulations, rendering, and semi-empirical physics models. Based on these simulators, we design corresponding inverse problems that can be categorized into parameter identification, inverse design, and optimal control. Finally, we conduct extensive experiments to compare the performance of different optimizers, including regular first-order methods, approximate second-order methods, as well as global optimizers, on these inverse problems, and analyze the results to provide insights on how to choose and design optimizers for differentiable programming. We hope such benchmarks can inspire the development of more effective optimizers, and further promote the applications of differentiable programming in various scientific and engineering domains.

Accurate quantum number assignment for calculated molecular energy levels is a critical bottleneck in generating line broadening parameters for comprehensive line lists for radiative transfer applications. We present an automated pipeline for assigning Air Force Geophysics Laboratory (AFGL) quantum numbers to CO2 calculated rovibrational states lying below 15,000cm$^{-1}$ across all 12 stable isotopologues. A GraphSAGE graph neural network is trained transductively on empirical (MARVEL) energy levels, exploiting inter-isotopologue perturbation chains and intra-isotopologue rotational ladder edges to propagate assignment information to unlabelled calculated states. Physical uniqueness is enforced locally by a Hungarian algorithm solver operating within groups of states sharing the same polyad, rotational quantum number, and parity. A five-generation bootstrap loop iteratively promotes high-confidence predictions into the training set, expanding coverage without additional labelling effort. The pipeline assigns 224,650 previously unlabelled states over 12 isotopologues, accounting for 10.7$\%$ of all available states (including MARVEL-derived levels), with coverage now increased to 97.4$\%$ below 5000cm$^{-1}$. The pipeline includes a novel decision tree method for converting AFGL to Herzberg notation in asymmetric isotopologues, while the architecture and Hungarian uniqueness enforcement are applicable beyond CO2, any molecular system with a conserved polyad-like quantum number and a large body of unlabelled computed states is a natural target for this approach, suggesting a pathway toward automated quantum number annotation for the next generation of large-scale computed line lists.

Thixotropic and viscoelastic materials both retain their deformation history and are difficult to distinguish. However, a stationary linear response obeys the Kramers-Kronig relations. A structure that evolves during a sweep renders the spectrum inconsistent with them, so the residual from one chirp and no model measures nonstationarity. It survives the small-amplitude limit, returns to its baseline at any stationary state, and, for bounded restructuring, peaks near the structural time. We verify this on eight models and demonstrate it on published spectra of a thixotropic silica suspension.

Cohesive zone models are widely used to describe fracture and interfacial failure, yet most formulations prescribe problem-specific analytical traction-separation laws together with phenomenological rules for unloading and reloading, leading to specialized models for different cohesive behaviors. This work develops a unified learnable cohesive formulation within the generalized standard materials framework, in which the response is generated from learned constitutive functions while the underlying thermodynamic structure remains fixed. The surface free energy is decomposed into active and contact contributions, and irreversible damage evolution is governed by a learned mode-dependent damage resistance. The active energy is represented by an input-convex neural network, while the inverse damage resistance is represented by a monotone neural network. Convexity, monotonicity, normalization, and damage irreversibility are incorporated directly into the constitutive representation. Direct parameterization of the inverse resistance yields an explicit damage update and avoids local nonlinear inversion during constitutive evaluation. Material-point studies show that the formulation can represent qualitatively distinct cohesive responses, including plateaus, extended softening tails, irregular softening, nonlinear unloading, distinct Mode I and Mode II behaviors, and several classical mixed-mode cohesive laws. The framework therefore replaces law-specific model construction with a single thermodynamically structured representation capable of learning cohesive responses of broad functional complexity from data.

Conservative dose remapping can be formulated by transporting mass and deposited energy across nonmatching grids, but fixed sampling uses the same number of points per active source cell regardless of case difficulty. We developed conformal multiresolution error control (CoMERC), a fail-closed multilevel quasi-Monte Carlo (QMC) controller for reference-relative target-cell allocation error in a fixed piecewise-constant source model with known deformation. Two randomized nested replicates were evaluated at a probe level of 4 and candidate levels of 8 and 16. Four endpoints were controlled jointly: reference-mass-weighted global, high-gradient, and density-gradient-threshold root-mean-square error, plus eligible-cell maximum absolute error. A split-conformal multiplier was calibrated from 240 synthetic cases, frozen, and evaluated on 400 independent cases from the same generator and 120 cases from a prespecified shifted generator. In the primary set, joint coverage was 394/400 (98.50%; one-sided 95% lower limit, 97.06%), 388/400 cases (97.00%; lower limit, 95.18%) received an output, and 0/388 released outputs exceeded any tolerance (one-sided 95% upper limit, 0.769%). Final actions were level 8 for 190 cases, level 16 for 198, and ABSTAIN for 12. The mean of the case-normalized, policy-implied production-sample ratios was 0.5844, with each case normalized to always running both randomized replicates through level 16; the prespecified 95th-percentile bootstrap upper limit was 0.6194. All seven primary gates passed. In the shifted-generator set, 113/120 cases were released and one released output exceeded a tolerance. CoMERC provided marginal finite-sample error control and lower modeled production-sample work in the locked synthetic population, but not patient-level, registration-level, conditional-on-release, or clinical safety validation.

Modern design optimization frameworks aim first and foremost for models with the most accurate predictions without balancing computational overhead. It remains a reason why scaled architecture and multidisciplinary design optimization problems are difficult to address, even with sample-efficient Bayesian optimizers. In this paper, a metric quantifying the computational energy footprint is introduced within a Bayesian optimization framework to guide the parameter setting of a model towards configurations that balance both performance and frugality. The computer experiments highlighted existing tradeoffs between optimum convergence and the underlying energy footprint, and sometimes resulted in both a better-found optimum and lower energy consumption.

We present a comprehensive observational analysis of modified Gauss–Bonnet, or $f(G)$, gravity by investigating both the background cosmological expansion and sub-horizon linear matter perturbations. We consider two viable functional forms: an arctangent parameterization (Model~I) and a generalized polynomial power-law model (Model~II). Using a Markov Chain Monte Carlo (MCMC) ensemble sampler, we constrain their parameter spaces through background and structure-growth observations. Our analysis employs cumulative combinations of Cosmic Chronometers (CC), the Pantheon+ Type Ia Supernovae compilation (PP), Redshift-Space Distortions (RSD), and the Year-1 Baryon Acoustic Oscillation measurements from the Dark Energy Spectroscopic Instrument (DESI BAO). The inclusion of DESI BAO data produces a noticeable downward shift in the preferred values of the Hubble constant, $H_0$, and present matter density parameter, $\Omega_{m0}$. For Model~II, the full analysis yields $H_0=64.02^{+4.13}*{-3.08},\mathrm{km,s^{-1},Mpc^{-1}}$ and $\Omega*{m0}=0.249^{+0.047}_{-0.038}$. We further assess the statistical performance of the models relative to $\Lambda$CDM using $\Delta\mathrm{AIC}_c$, $\Delta\mathrm{BIC}$, and $\Delta\mathrm{DIC}$. For Model~II, the full dataset gives $\Delta\mathrm{AIC}_c=4.233$ and $\Delta\mathrm{DIC}=5.971$, favoring the $\Lambda$CDM baseline. At the perturbation level, both models exhibit stable growth histories compatible with large-scale structure observations. The models predict transitions in the late-time expansion dynamics at $z\approx0.5005$ and $z\approx0.6086$ for Models~I and II, respectively, highlighting differences in their cosmological evolution.

The present study analyzes encounters of a vertical vortex with a tailless NACA 0012 delta wing with a sweep angle of 60 degrees at an angle of attack of 10 degrees and Reynolds number 1000 using direct numerical simulations. Motivated by the need to fly in complex settings such as urban canyons, mountainous areas, wildfires, or vehicle swarms, we focus on the influence of a vertical vortex gust on the delta wing, complementing previous efforts that predominantly studied spanwise and streamwise vortex encounters. The vertical orientation can impose strong transient loads on all six degrees of freedom, and particular attention is devoted to connecting these loads to the flow response. By varying the gust's lateral position, size, and strength, we find a fundamental process that characterizes vertical vortex gust encounters. The gust consistently imprints a low-pressure core on the wing surface. The evolving core creates an adverse pressure gradient that distorts the baseline skin-friction pattern and induces separation that lifts the boundary layers. These lifted boundary layers are elongated and twisted by the gust's strain into lobed vortical structures, which are shown by force element analysis to make a critical contribution to the lift. As the gust's lateral position is varied from root to tip, the low-pressure core and the induced flow separation are increasingly localized around the leading edge closest to the gust, modifying the lobe evolution and leading to complex dependence of the force and moment response on gust position compared to the largely monotonic dependence on gust size and strength.

The European Southern Observatory's Extremely Large Telescope (ELT), the 39-m telescope under construction in Chile, forms its entrance pupil from 798 hexagonal segments that the six 0.5-m-wide spider arms fragment into six "petals". The low-wind / island effect, adaptive-optics residuals, and mechanical drift imprint an independent piston on each petal. These differential-piston (petal) modes remain largely undetected by the ELT's baseline continuous-pupil wavefront sensors, a major bottleneck for diffraction-limited, high-contrast imaging. This paper presents a complete theoretical model of a single-mode photonic discrete beam combiner (DBC) configured to sense the six petal pistons. The model constructs the coupled-mode description of a two-dimensional evanescently coupled waveguide lattice with six inputs and forty-five outputs and derives its visibility-to-pixel matrix (V2PM); treats the pupil remapping that routes one sub-aperture per petal into the six inputs; develops the five-dimensional petal-mode basis of the fragmented pupil; and propagates the petal modes through the DBC to quantify the sensitivity. Optimising the input-port spacing and interaction length by minimising the V2PM condition number yields a well-conditioned device (CN = 10.4). It recovers petal pistons with unit linear gain over the full +/-lambda/2 (+/-800 nm) unambiguous range and separates the five modes with negligible cross-talk (< 10^-3). For 1% photometric noise, the reconstructed-piston error is 4.7 nm rms at lambda_0 = 1.6 micron. Tested against petal amplitudes from end-to-end ELT simulations (~40-300 nm rms atmospheric residual; ~0.3-1 micron low-wind events), the DBC recovers each piston to a few nm rms at H band, with a two-wavelength extension capturing the larger events. The DBC is therefore a compact, chromatically robust petalometer for the fragmented ELT pupil.

Accurate estimation of unsteady aerodynamic flows from sparse measurements remains a fundamental challenge, particularly in the presence of strong disturbances, unknown body kinematics, and incomplete observations. This study presents a data-driven sequential estimation framework for joint reconstruction of unsteady flow fields, aerodynamic loads, and airfoil kinematics from sparse measurements. The approach combines a kinematics-aware nonlinear flow autoencoder with online filtering of streaming measurements. In the resulting reduced-order representation, the forecast and observation operators are learned directly from data, with each forecast-assimilation cycle requiring only a few milliseconds. The framework is evaluated on two-dimensional incompressible flow over an airfoil subjected to random vortical gusts while undergoing arbitrary pitch-up motions. Results demonstrate accurate reconstruction from surface pressure sensors, vertical lines of vorticity sensors, and synthetic velocimetry data with representative shadow regions. The transient informativeness of pressure sensors is quantified through their time-varying contributions to dominant observation modes. While lift is reconstructed from the leading modes, drag, pitch angle, and angular velocity require higher modes. Incorporating limited off-body measurements alongside surface pressure improves observability and reduces estimation error and uncertainty.

Accurate detection and quantification of light-element isotopes, particularly hydrogen (H) and lithium (Li), are essential across chemistry, materials science, geology, nuclear energy, forensics, and defense applications. Their isotopic compositions provide insight into fuel cycles, materials degradation, mass transport, and environmental processes. Hydrogen and Li isotopes are especially relevant in nuclear environments because neutron-induced reactions directly connect their isotopic inventories. Despite their importance, analysis of these light isotopes remains challenging due to their low atomic mass, high mobility, and, in some cases, radiological constraints. While some existing analytical techniques can detect and measure H and/or Li, they typically require extensive sample preparation, laboratory-based instrumentation, or extended analysis times that preclude real-time monitoring capabilities. To address these limitations, optical spectroscopy of laser-produced plasmas offers a promising solution, enabling field-deployable, standoff sensing of isotopic signatures with real-time analysis capability and no sample preparation requirement. This Topical Review summarizes fundamentals and recent advances in H and Li isotopic analysis using optical spectroscopy, with emphasis on laser-produced plasmas generated by laser ablation of solid targets. Key concepts relevant to isotope-resolved optical measurements are introduced, including isotope shifts, fine and hyperfine structure, and line-broadening mechanisms. Progress in optical emission, laser induced fluorescence, and absorption spectroscopy is reviewed for H and Li isotopes.

First-principles, electronic-structure simulations provide direct access to ground-state, global system properties. In the context of computational electrochemistry these are, e.g., bias-dependent energies, free energies, work functions, and capacitances. However, a comparably systematic local description of how these quantities respond to biasing via interfacial charging is still missing. Here, we introduce the screened electrochemical Born effective charge (SEBEC), a mixed total-energy derivative that measures the force response of an atomic degree of freedom to interfacial charging under electrochemical boundary conditions. SEBECs play the role for electrochemistry that Born effective charges play in the modern theory of polarization: they provide an unambiguous decomposition of global response and generate the same formal structures for structural relaxation, capacitance, and electrochemical Stark tuning that are familiar from solid-state response theory. The broader implication is a local response framework that brings electrochemical interfaces closer to the conceptual rigor long established for insulating solids.

We investigate the structural information retained in the distribution of explosion ion trajectories from proteins subjected to strong ionization. Using molecular-dynamics simulations and machine-learning analysis benchmarked against an analytical approach, we show that low-resolution structural information can be retrieved from the explosion distributions alone. An ensemble of convolutional neural networks trained on simulated spherical ion maps recovers the radius of gyration and the three semi-axes of an ellipsoidal molecular envelope with prediction errors of 1.2 Å and 1.5 Å, respectively, while an analytical ellipsoid charge model returns similar estimates and performs best for globular structures. We further investigate how higher-level structural information and symmetries can be extrapolated from ion measurements. This study supports the viability for structural determination of single proteins without large-scale X-ray facilities.

Materials with full anisotropic thermal conductivity tensors are widely used in advanced applications, creating a need for dedicated characterization. Such tensors contain six independent components: three diagonal conductivities and three non-zero off-diagonal couplings. However, whether thermoreflectance measurements on a single crystalline orientation can uniquely determine an arbitrary tensor remains unclear. Here, we show that every fully anisotropic thermal-conductivity tensor admits a Schur-equivalent tensor with zero cross-plane couplings ($k_{xz}=k_{yz}=0$). The two tensors produce identical surface thermal responses, and this equivalence persists as frequency, delay time, beam geometry, and spatial offset vary. We further demonstrate that parameter-identifiability assessments based on heuristic interpretations of sensitivity trends or singular-value decomposition (SVD) of the sensitivity matrix cannot, by themselves, rule out globally equivalent solutions in fully anisotropic systems. Combining systematic analysis with numerical validation, we provide guidance on selecting signals and measurement orientations for full-tensor characterization. Specifically, measurements at three mutually orthogonal crystallographic orientations provide complementary information for full-tensor recovery. These findings clarify the limits of single-surface measurements and establish a systematic approach to characterizing fully anisotropic thermal-conductivity tensors.

Can attention move a shock across several cells in one update without breaking it? We develop a conservative, fixed grid finite-volume scheme in which a CFL-conditioned attention flux selects upstream information according to the transport required by the current time step. One-dimensional inviscid Burgers transport is used as the central mechanism test: the same learned flux remains reliable in the conventional small-step regime and, with a time step four times larger, preserves sharp shocks while using one stage per update. A standard fifth-order WENO scheme with third-order strong-stability-preserving Runge-Kutta time integration (WENO-5+SSP-RK3) is included alongside controlled Forward Euler comparisons to separate flux selection from time integration. The learned attention shifts upstream with the local transport reach and becomes more selective near shocks; inference-time interventions and retrained ablations show that transport-scale information and state-dependent selection contribute directly to performance. Directional two-dimensional scalar Burgers transport and the one-dimensional shallow-water system then test whether the conservation-scale-selection principle transfers beyond the original scalar setting. The results support attention as a learnable information stencil for conservative large-step shock transport, while identifying finite candidate reach and problem-dependent robustness as the present limits.

Humans see the polarization of light through entoptic percepts arising from the macula's Henle fiber layer, where xanthophyll pigments absorb preferentially across the radiating fibers. From the layer's complex dielectric tensor alone, Maxwell's equations in Berreman $4\times4$ form yield its Mueller matrix, set by four scalars $\{A,B,C,D\}$. Its intensity channel depends only on the dichroic pair $A$ and $B$, which fix the percepts at a maximum contrast $|B|/A\approx0.05$. One relation generates the whole dichroism-mediated family: Haidinger's brushes under a uniform field, their dark arms perpendicular to the $\mathbf{E}$-vector; fractured brushes under spatially varying fields; and $N$-fold brushes under vector-vortex illumination.

Simulating the propagation of a large beam with high spatial resolution is a common challenge in optics due to both computational time and memory demands. While computational time sets a practical limit on what one can simulate, memory resources are an absolute hard limit. In this paper, we report a novel method to overcome a gap in spatial scale between the beam and the relatively small optical disturbance that needs to be modeled, such as those due to particulate contamination. The method splits the entire propagation chain into to two paths: a specular field propagation on coarse grids and scattered fields on fine grids. Especially for the latter, we dynamically apply high-resolution local patches for individual particles. Not limited to particulate contamination, this method is applicable to any tiny discontinuous structures on the beam propagation path without memory overflow triggered.

Extreme salinity events in Florida Bay are a central concern of the Comprehensive Everglades Restoration Program (CERP). A large-scale seagrass die-off in the late 1980s, driven by hypersaline conditions following reduced freshwater delivery, made reliable salinity prediction essential to restoration planning. The Bay Assessment Model (BAM) is a hydrologic model that simulates salinity across 54 idealized basins representing Florida Bay. It is mass-conservative apart from where required for model stability. Interbasin fluxes are computed from hydraulic gradients across shoals, and each basin receives direct rainfall and evapotranspiration forcing. Along the shoreward boundary BAM is driven by observed water levels from the Everglades Depth Estimation Network (EDEN), or by output from upstream restoration planning models. Along the marine margins, tidal boundary conditions from NOAA subordinate station harmonic constituents are superimposed on a regional mean sea level anomaly. BAM is implemented entirely in Python and is open source. Over the extended 1999-2026 record the domain-wide mean water level bias is -0.024 m with a mean RMSE of 0.092 m, and the domain-wide mean salinity bias is 0.00 ppt with a mean RMSE of 6.21 ppt. Model bias varies systematically with hydrologic regime, increasing in magnitude from drought to wet conditions and reversing sign at one central bay basin, constraining the interpretation of anomaly-based metrics. Applying uniform offsets at the Everglades shoreward boundary yields salinity responses of 2-4 ppt in the northeastern bay, attenuating toward the marine margins; a fuller treatment of restoration and sea level scenarios is reported separately. BAM provides a transparent, efficient, and community-accessible tool for evaluating the salinity consequences of Everglades restoration actions in Florida Bay.

NASA's Habitable Worlds Observatory (HWO) will require ultraviolet, optical, and near-infrared instruments that are simultaneously compact, mechanically and thermally stable, high-throughput, and replicable at large channel counts. Space-qualified photonic integrated circuits (PICs) and optical fibres are key platforms that can deliver these properties: they manipulate light at the diffraction limit within micron-scale single-mode waveguide circuits. An astrophotonic instrument fully guides the starlight from focal plane to detector, eliminating scatter and ghosts and offering excellent stability with no moving parts. This paper examines how astrophotonics can address the principal science drivers of HWO through four topics: (I) the HWO observing modes best implemented with photonic instruments; (II) the waveguide and fibre materials that can span the demanding 100nm-2.5{\mu}m HWO wavelength range; (III) the potential of arrayed-waveguide-grating (AWG) spectrographs to reach the resolving powers and throughputs HWO science requires; and (IV) the steps needed to space-qualify PICs and fibres against radiation, thermal cycling, vacuum, and launch loads. For each topic, concrete technical requirements are extracted and the current Technology Readiness Level (TRL) is assessed. The main contribution of this paper is to unify these science drivers, candidate-aperture photon budgets, UV-to-near-infrared material platforms, AWG architectures, and space-qualification requirements within a single photon-budget framework that pairs each claimed benefit with a measurable requirement, a TRL, and an environmental test that can retire it. A phased roadmap advances the critical photonic components from their present TRL~2 to -5, depending on platform and application, to the TRL~6 needed at HWO's instrument-definition gate.

For computing the ground state of the defocusing Gross–Pitaevskii energy, we propose and analyze two efficient schemes based on the Davis–Yin three-operator splitting, which treats the potential and interaction terms explicitly, and the kinetic energy by a resolvent. One iteration costs one inversion of $I-\gamma\Delta$ for the first scheme, and of $I-\gamma\Delta+\gamma V_1$ for the second, with $V_1$ denoting the separable part of the potential, and on structured meshes both operators can be inverted by simple fast GPU solvers. For monotone discrete Laplacians, including the second-order finite difference scheme and the lumped linear finite element method on simplicial meshes with suitable angle conditions, we prove global convergence to the unique positive discrete ground state, for every positive normalized initial vector, for any constant step size below an explicit threshold. In contrast, the methods previously proven to converge globally to the ground state all invert a more difficult elliptic operator that depends on the iteration variable. In three-dimensional tests with up to $999^3$ unknowns on one GPU, a simple variable step size rule makes the proposed splitting schemes efficient in practice, comparable in wall-clock time to Riemannian conjugate gradient methods that also invert only a shifted Laplacian operator, and much more robust with respect to the choice of the initial guess.

High-order entropy stable (ES) methods have attracted much interest in the computational fluid dynamics (CFD) research community over the past decade, as we seek to simulate practical engineering applications with high accuracy and robustness. As industry delves further into supersonic flight and space travel, we need schemes capable of modelling flows with multiple chemical species that interact and mix within the fluid. As such, there is a considerable amount of effort currently being focused on developing high-order ES schemes for multi-species (MS) flow. A key ingredient for these entropy stable schemes is the two-point flux, which determines the properties of the resulting high-order discretization. As such, the numerical scheme can possess important properties such as entropy conservation and kinetic energy preservation only if the two-point flux also mirrors these properties. At present, in the literature, the two-point fluxes used for high-order ES multi-species solvers are not kinetic energy preserving (KEP). In this short communication we develop a two-point flux that is both entropy conserving (EC) and kinetic energy preserving by adapting Ranocha's single-species methodology for multi-species. The newly proposed EC and KEP flux is tested against existing two-point fluxes in the literature using the density pulse advection and Taylor-Green vortex cases. It is shown that EC and KEP flux retains both properties while the other fluxes only retain one of the desired properties. Since the two-point flux dictates the properties of the resulting scheme, the proposed flux is the ideal option for simulating multi-species compressible flow.

We derive a unified set of scaling equations for Bose-Einstein condensates in time-dependent harmonic traps, connecting the weakly interacting Gaussian regime to the strongly interacting Thomas-Fermi regime. The exact Gross-Pitaevskii ground-state density is taken as a fixed profile in rescaled coordinates, with its dynamics described by three scaling factors corresponding to compression or expansion along the three spatial directions. The resulting equations contain no adjustable parameter, since their coefficients are determined once from the initial state. They recover analytically the Gaussian variational and Thomas-Fermi scaling equations in their respective limits and satisfy an axis-resolved virial theorem. The approach also yields the global phase and the low-lying collective-mode frequencies of the condensate. We benchmark the model against three-dimensional Gross-Pitaevskii simulations and against measured expansion energies. The model remains accurate across all interaction regimes, in strongly anisotropic traps, and along time-dependent sequences including a relatively fast quench, up to the point where the underlying frozen-profile hypothesis, shared by all three scaling approaches, breaks down.

Transverse light localization of Anderson type through disorders has made intriguing fundamental topics. Toward this, we found that in the disordered porous Si (PSi) a nearly "zero reflection" (from 450 to 900 nm) can be observed. The localization length versus disorder parameter is experimentally visualized and calculated based on a self consistent theory and solution of stochastic Helmholtz equation for electric fields traveling in a transverse disordered media with the results determined to be in a reasonable agreement with experiments. Based on these calculations for different pore diameters in a constant order parameter, diffusion length can vary from 260 nm to 2.4\mu m, accounting as controllability of such structure for further applications in photonic devices. Reflection from ordered PSi structure are evaluated that shows no similar responses to those seen in disordered PSi.

Time-dependent mantle-dynamics inversion must address the high dimensionality of the initial state, nonlinear rheology, and gradient propagation through long-term thermo-mechanical evolution. We develop ADEPTS, a two-dimensional staggered-grid finite-difference framework for mantle-dynamics inversion based on automatic differentiation. The forward model solves incompressible Stokes flow, temperature advection-diffusion, and compositional advection with temperature- and strain-rate-dependent viscosity and plastic yielding. For the nonlinear Stokes system, we compare two gradient strategies: unrolled differentiation through a fixed number of Picard iterations and implicit differentiation of the converged discrete residual equations. Numerical experiments show that unrolled differentiation remains stable even when the nonlinear solve is not fully converged, whereas implicit differentiation requires sufficiently accurate nonlinear solutions; otherwise gradient consistency and optimization convergence deteriorate. With sufficiently converged solves, implicit differentiation recovers accurate gradients and reconstruction quality comparable to unrolled differentiation. Joint thermo-chemical twin experiments show that ADEPTS can simultaneously recover a high-dimensional initial temperature field and low-dimensional physical parameters, including compositional density, reference viscosity, and stress exponent, while fitting final-time temperature, surface horizontal velocity, and surface normal stress. These results demonstrate the feasibility of differentiable time-dependent mantle-dynamics inversion and clarify the different convergence requirements of unrolled and implicit differentiation.

We present a Godunov-type mesh-free hydrodynamics scheme for the numerical simulation of weakly compressible single- and multiphase flows. The method employs a consistent second-order spatial reconstruction of the primitive variables at the inter-particle interface, applies slope limiting, and solves the moving Riemann problem using a subsonic HLLC approximate Riemann solver. Particles are advected with a quasi-Lagrangian material velocity incorporating an inherent self-relaxation mechanism that suppresses the formation of anisotropic particle distributions. Near material interfaces, one-sided renormalized gradients are used to handle density discontinuities and slope limiting is applied for each phase independently. The Lagrangian character of material interfaces is preserved by constraining the relaxation mechanism to act tangentially to the interface. For same-phase particle interactions, the Riemann problem is centered at the arithmetic mean of the two material velocities. For cross-phase interactions, it is centered at the contact-wave velocity. This choice naturally eliminates spurious mass fluxes across phase boundaries. The methodology is validated on a set of challenging benchmarks, including the inviscid Taylor-Green vortex, material interface advection, surface gravity waves near hydrostatic equilibrium, the Kelvin-Helmholtz instability, and the Rayleigh-Taylor instability under gravitational stratification.

Cells in connective tissues reside within the extracellular matrix (ECM), which consists of a fibrous mesh that exhibits non-linear strain-stiffening behavior, driven by a transition from bending-to-stretching-dominated deformation. While bulk rheology captures macroscopic mechanical properties, cells actively sense and respond to local microscale heterogeneities and stiffness anisotropy in their environment. Characterizing ECM micromechanics is therefore essential for understanding the mechanical cues experienced by cells. This study quantifies local stiffness anisotropy in stretched fibrous gels by combining experimental and numerical approaches. Experimentally, we utilized optical tweezers microrheology to measure local stiffness in fibrin gels subjected to uniaxial stretch. The gels demonstrated gradual local stiffening along both the tensile and perpendicular axes, with a more profound increase along the tensile axis, resulting in local anisotropy. To investigate the physical parameters driving this phenomenon, we developed a 3D finite element model of a discrete random fiber network, successfully replicating the experimental local stiffening and anisotropy. Numerical analysis further revealed that within the sub-isostatic region, both fiber thickness and network connectivity strongly influence local anisotropy: slender fibers and higher connectivity amplify the anisotropy by up to an order of magnitude. This contributes to the formation of a highly anisotropic local environment, thereby playing a significant role in directing mechanically driven biological processes, such as cell migration and durotaxis. Our simulations also indicate that local micromechanical responses may differ from the material's global stiffening behaviors, highlighting the need for characterization at the microscopic scale.

The inverse design of crystalline materials ultimately seeks structures with desired physical properties. However, for many functional responses, a favorable numerical value is meaningful only when supported by the symmetry of the underlying crystal. Without the appropriate crystallographic constraints, an apparent response may be ill defined, accidental, or not symmetry protected. Here we introduce SPARC, a symmetry- and property-aware reinforcement learning framework that optimizes physical objectives while preserving the structural conditions required for their realization. We demonstrate SPARC on two complementary tasks. The first targets strong uniaxial dielectric anisotropy, a tensorial response that is well defined only within appropriate crystal classes. The second maximizes the spectroscopic limited maximum efficiency, a scalar device-level objective without a prescribed symmetry class, allowing the framework to identify favorable crystallographic motifs. These results show that symmetry is not merely an additional design constraint, but a physical foundation for generating candidates with meaningful, robust, and realizable functional properties.

Laser Powder-blown Directed Energy Deposition (LP-DED) offers flexibility for process and materials and high productivity (~5 kg/h), but process-induced pores often compromise mechanical properties. This study utilizes in-situ X-ray synchrotron imaging to compare pore formation mechanisms in Ti-6Al-4V (Ti64) and stainless steel 316L (SS316L), focusing on the interplay between particle dynamics and thermophysical properties. Four distinct pore formation mechanisms were identified, with most large pores originating from the closure of cavities formed behind incident particles impinging on the melt pool. High Weber number (We >> 1) governs this behavior, indicating that particle inertia, rather than thermophysical property differences, is the primary driver of large pore formation. The study demonstrates that increased energy density leads to larger melt pool volumes, facilitating deeper particle penetration. This greater penetration depth directly correlates with increased pore diameters. While thermophysical properties secondarily influence pore-formation frequency and cavity symmetry, particle inertia remains the dominant factor. These findings provide a physically grounded basis for understanding and controlling porosity in powder-blown DED processes.

Inverse design is a powerful computational tool for generating nanophotonic devices that should outperform their traditionally designed counterparts. However, translating these theoretical gains into physical devices is challenging because the irregular and non-intuitive features that make inversely designed structures so effective also make them exceptionally difficult to fabricate, typically necessitating substantial process optimization unique to each device design. We describe a versatile two-step nanofabrication process that achieves pattern transfer fidelity in excess of 90% for each of three distinct inverse device designs with no device-specific process optimization.