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arXiv2026

Learning the Constitutive Behavior of Materials via Neural Operators and Causal Attention: Case Studies in Plasticity and Damage

Classical constitutive modeling of path-dependent inelastic materials relies on internal state variables whose evolution equations must be postulated based on domain knowledge and calibrated against experimental data. However, in many practical settings, the relevant internal variables are typically not measurable in experiments, and the constitutive response must be inferred entirely from measured strain-stress data without any prior knowledge of the material's internal state. We propose a data-driven constitutive modeling framework based on the concept of a material operator, which treats a deforming material as a functional mapping from its entire strain history to the corresponding stress response. In contrast to traditional autoregressive or recurrent formulations, the model is trained directly on full loading paths as function-to-function mappings, predicting complete stress trajectories in a single parallel forward pass. Temporal path dependence is enforced through a causally masked attention mechanism embedded within the operator, which restricts the model's attention to past material states while preserving computational parallelizability. Spectral convolutions provide discretization-invariant representations in the frequency domain, while causal attention captures highly adaptive, non-local history dependence. Furthermore, sinusoidal activation functions are used to resolve the strong nonlinear transitions inherent in inelastic regimes. The framework is evaluated across multidimensional, rate-independent material models exhibiting complex phenomena, with an emphasis on nonlinear plasticity and ductile damage accumulation. The results demonstrate accurate and robust predictions of irreversible deformation mechanisms while simultaneously achieving resolution invariance and excellent parallel efficiency.

cs.LG↗
arXiv2026

A meshfree solver for coupled bulk-surface problems with self-organizing surface geometry

In many systems, the interaction between a deformable surface or interface and the surrounding bulk fluid is coupled with intrinsic spatiotemporal dynamics within the moving surface. Examples include tumor growth, biological tissue morphogenesis, cardiac mechanics, multi-phase surfactant chemistry, additive manufacturing, clothing wear-and-tear, and reactive combustion flows. Solving such problems requires both geometric computing algorithms to track and resolve the surface and numerical methods to solve the coupled governing equations in the surface and the surrounding bulk phase. Here, we present a fully meshfree numerical solver for such coupled bulk-surface problems with deformable interfaces. The presented solver tracks the surface implicitly, solving for the dynamic surface geometry based on stress balance coupled to surrounding fluid phases. We show convergence for a mass-conserving case on a growing sphere and solve bulk-surface problems with incompressible Navier-Stokes fluids coupled to in-surface nonlinear reaction-diffusion dynamics. Finally, we show a model of biological morphogenesis, solving simultaneously for the dynamic surface shape and the fields on the curved surface with two-way coupling.

cs.CE↗
arXiv2026

Eliciting ESG Preferences for Reinforcement Learning-Based Portfolio Optimization

Modern portfolio management increasingly demands a balance between traditional risk-adjusted returns and strict Environmental, Social, and Governance (ESG) mandates. Current Reinforcement Learning (RL) approaches typically optimize for a single ESG provider, neglecting the significant divergence in rating methodologies across the industry and the unintuitive nature of manually weighting conflicting objectives. This paper addresses these limitations by formulating ESG-aware portfolio optimization as a Multi-Objective Reinforcement Learning (MORL) problem that simultaneously incorporates ratings from three distinct ESG agencies. To bridge the gap between high-dimensional algorithmic trade-offs and human decision-making, we integrate a Preference Elicitation framework using Gaussian Processes. This system enables practitioners to infer their latent utility functions through intuitive pairwise comparisons of candidate portfolios based on their Sharpe ratios and aggregate ESG scores. We systematically evaluate our framework by employing Large Language Model (LLM) personas to simulate Portfolio Managers operating under varied regional contexts. Empirical results using historical market data reveal that regional backgrounds fundamentally shift the derived preference weights. For instance, European-based personas tend to prioritize ESG alignment over financial returns, while Texas-based personas favor risk-adjusted performance. This work offers a highly adaptable framework that successfully aligns multi-objective algorithmic trading with diverse, real-world human sustainability preferences.

q-fin.PM↗
arXiv2026

Dimensional hyperreduction of nonlinear finite element models via empirical cubature with manifold-adaptive weights

Nonlinear-manifold reduced-order models for parametrized finite element problems can achieve substantial compression both in the number of generalized (latent) coordinates and, through sampling-and-weighting hyperreduction, in the number of sampled elements/integration points. Yet current sampling-and-weighting approaches employ weights that remain fixed over the solution manifold. We contend that this restriction leaves hyperreduction potential untapped: allowing the weights to vary continuously and nonlinearly with the latent coordinates can further decrease the number of sampled spatial entities. To exploit this possibility, we propose the Manifold-Adaptive-Weight Empirical Cubature Method (MAW-ECM). Starting from a feasible fixed-weight ECM rule, a greedy pruning strategy removes sampled entities through convex quadratic weight-redistribution problems enforcing local conditions and positivity. The method is assessed on two nonlinear benchmarks: homogenization of a metamaterial unit cell exhibiting negative incremental stiffness, and a history-dependent continuum-damage problem. In both cases, the nonlinear manifold is constructed from an initial linear compression followed by an input-informed identification of the latent coordinates as general linear combinations of the retained modal amplitudes, incorporating graph information when relevant to seek the intrinsic dimensionality of the solution manifold. We show that combining the nonlinear-manifold representation with MAW-ECM reduces the number of sampled integration points by more than two orders of magnitude relative to the corresponding standard linear reduced model. Relative to the fixed-weight manifold models alone, the adaptive weights eliminate approximately 80% of the remaining points in the homogenization benchmark and more than 97% in the damage benchmark, while essentially preserving their accuracy.

math.NA↗
arXiv2026

The Analyst in the Prompt: Role, Retrieval, and Memory Biases in LLM Financial Analysis

Large Language Models (LLMs) increasingly use user context such as memory, profiles, and role prompts to personalize their responses. This personalization can affect evidence-based judgment: the same evidence may lead to different conclusions under different user contexts. Finance provides a high-stakes setting to study this problem because decisions often depend on interpreting long and complex documents. We test this using 3,575 SEC filings across twelve LLMs. We compare persona-conditioned retrieval, neutral retrieval, and memory-framed context to separate the effect of evidence selection from the effect of interpretation. We find that most user-context spillover comes from how models interpret the same evidence under different roles, rather than from retrieving different evidence. We then test two simple mitigation strategies: expressing the same investor mindset as a user profile instead of an assistant role, and separating evidence-based and personalized outputs. Both reduce spillover, but neither removes it completely, and their effectiveness varies substantially across models.

cs.CL↗
arXiv2026

Physics-Informed Neural Networks for Depth-Averaged Granular Avalanche Dynamics on Curved Topography

Physics-informed neural networks (PINNs) provide a mesh-free framework for solving governing equations, but their application to granular avalanche dynamics over curved terrain remains largely unexplored. This study extends a depth-averaged PINN formulation based on the Savage-Hutter equations to an exponentially curved chute with spatially varying inclination and a strain-rate-dependent Mohr-Coulomb earth-pressure closure. The model is validated against measured front- and rear-edge trajectories from a laboratory granular-avalanche experiment, with selected observations withheld from training. A staged temporal curriculum proved essential for accurate prediction, reducing the held-out trajectory error by approximately two orders of magnitude compared with training over the full time domain from the outset. Sparse-data experiments further showed that observation placement was more influential than observation number within the configurations tested. Four observations bracketing the transition from acceleration to deceleration achieved nearly the same accuracy as the eight-observation reference configuration, whereas observations clustered at early or late times performed poorly. The results demonstrate the importance of both training strategy and informative data placement when applying PINNs to granular flows over curved topography.

cond-mat.soft↗
arXiv2026

Differentiable Randers-Finsler Eikonal Solvers

Fast and differentiable solvers for anisotropic and asymmetric distance fields are a key primitive in geometry processing, enabling gradient-based optimization over metrics, drift fields, and downstream objectives that depend on geodesic distances and geodesics. We present a differentiable Eikonal solver for Randers-Finsler metrics on Cartesian grids that combines the efficiency of a GPU-friendly column/row fast sweeping with exact gradients obtained by implicit differentiation. Our forward pass uses local one- and two-point upwind updates selected by a causality-valid stencil; the backward pass exploits the induced arrival-time ordering to solve the adjoint system via a single reverse-time back-substitution, avoiding unrolling (i.e., recording and differentiating through every solver iteration) and substantially reducing memory and runtime. We derive closed-form derivatives of the discrete updates with respect to arrival times and Randers parameters, and we enforce metric feasibility with differentiable projections that guarantee positive definiteness and valid drift magnitude. Although stencil selection is piecewise-smooth, we show gradients are stable under small perturbations and match finite differences away from stencil boundaries. We demonstrate accurate forward solutions and enable inverse problems such as recovering spatially varying anisotropic metrics and drift fields from sparse arrival-time supervision. Finally, we apply the method to learning data-driven spread models on real wildfire perimeters, illustrating scalability and the practical utility of differentiable Randers distance fields.

cs.CE↗
arXiv2026

Mesh-Native Physics-Informed Graph Surrogates for TCAD-in-the-Loop Design Space Exploration

High-fidelity TCAD simulation of drift-diffusion transport remains the workhorse of emerging FinFET device design, but it is computationally expensive, especially for 3D structures where runtime escalates steeply with mesh complexity. This sharply limits multi-objective design space exploration. Existing machine-learning surrogates map a fixed set of design parameters to a few scalar device metrics, discarding the underlying physics and losing transferability across device geometries and families. A physics-informed graph attention network (GAT) surrogate is proposed. It operates directly on the tetrahedral TCAD mesh and predicts, at every mesh node, the electrostatic potential together with the electron and hole quasi-Fermi levels, the fundamental unknowns of the drift-diffusion system. Training combines a data loss with finite-volume current-continuity residuals, embedding carrier-transport physics into the objective. Operating on the mesh as a graph, the surrogate inherits size generalization: a model trained on few-fin meshes applies unchanged to substantially larger arrays, bounded at inference only by GPU memory. Per-node uncertainty from a deep ensemble drives an active-learning loop that screens large candidate pools in seconds and forwards only the most informative designs for full simulation. Benchmarked against Sentaurus Device on multi-fin tri-gate FinFETs, the surrogate reproduces the three drift-diffusion fields with sub-volt per-field RMSE and reaches a per-design throughput orders of magnitude higher than the full simulator. The advantage grows with device size: on large multi-fin arrays that are prohibitively slow to simulate directly, inference still completes in under a second per device, enabling Pareto-front exploration across device scales infeasible for direct TCAD sweeps.

cs.LG↗
arXiv2026

On the Application of Hybrid Mixed Domain Decomposition Methods to Permanent Magnet Synchronous Machines

In this work, we study the application of a hybrid mixed domain decomposition(HMDD) method for the rotor-stator coupling of a permanent magnet synchronous machine. For this, we derive a variational formulation on the electric machine inspired by hybridized discontinuous Galerkin methods using a mixed magnetostatics problem, an affine material law and boundary conditions respecting the symmetry of the motor. We are then able to locate the resulting finite element method within the HMDD framework. This enables us naturally to transfer the well-posedness results and error estimates for the HMDD method to the finite element method considered in this work. Lastly, as a proof of concept, we consider an academic example and compare the resulting magnetic flux density and potential lines to their counterparts obtained by a well-established in-house code using iso-geometric analysis.

cs.CE↗
arXiv2026

Do simulated agents move like real people?

Human mobility is increasingly represented using synthetic populations that offer scalable alternatives when individual-level observations are unavailable or sensitive. Yet validation typically emphasizes aggregate statistics, which can obscure whether simulated agents traverse transportation networks in ways that resemble real travelers. Here, we develop a path-centric framework that combines direct path-level comparisons with higher-order network models to compare observed and simulated mobility on a shared metropolitan road network. Observed and simulated paths share broad statistical regularities and short-range memory. Beyond these similarities, however, simulated mobility underrepresents long paths, exhibits greater redundancy among long route sequences, covers a smaller and partly different portion of the network, and is more predictable overall. These discrepancies show that agreement in aggregate mobility patterns does not imply fidelity in how travelers move through the underlying infrastructure. Higher-order path analysis therefore offers a framework for validating synthetic mobility at the spatial and sequential scales relevant to scientific inference, urban planning, and policy.

cs.CE↗
arXiv2026

HarmoCore: Functional Latent Diffusion for Sparse Reconstruction of Oscillatory Wave Fields

Reconstructing oscillatory wave fields from scattered sensors is a severely underdetermined inverse problem. Beyond the challenges of general physical-field reconstruction, wave responses are complex-valued, frequency-sensitive, and highly oscillatory, while costly simulation and sensing often leave only extreme-sparse observations. Existing low-rank, operator, and diffusion approaches are largely designed for real-valued, smoother fields; dense pixel-space diffusion is particularly inefficient for oscillatory complex fields and difficult to scale to 3D. We propose HarmoCore, which places a generative prior in a compact, continuous, and structured wave-field latent. HarmoCore represents joint real--imaginary channels with Functional Tucker cores over shared continuous spatial bases, learns a frequency-conditioned core diffusion prior, and performs Diffusion Posterior Sampling directly in core space. At fixed sensor coordinates, the multilinear decoder induces an explicit likelihood guidance operator, avoiding dense pixel-space correction. Optional target-equation residual guidance further promotes physical consistency. Experiments on 2D Helmholtz, 2D synthetic wave fields, and 3D Helmholtz show substantial gains under 1%--2% sensing while remaining practical in three dimensions.

cs.LG↗
arXiv2026

VPID: An Integrated Framework for Vulnerability Prioritization and Intrusion Detection in Enterprise Networks

Small enterprises face increasingly serious threats to their internal networks but often lack the financial resources, computing capacity, and specialist staff required to deploy resource intensive security platforms. This paper designs and implements VPID, a lightweight framework for vulnerability prioritization and intrusion detection that consists of two principal modules: controlled vulnerability validation and intelligent intrusion defense. The first module uses OpenVAS for asset mapping and vulnerability identification, applies a decision tree to prioritize vulnerabilities, and employs a rule engine to generate targeted validation payloads. The second module captures network traffic using Scapy, analyzes it through a detection pipeline that combines a decision tree with multinomial Naive Bayes, verifies traffic assessed as high risk using Snort rules, and performs blocking and alerting through iptables. The evaluation uses 550,000 network flow samples containing normal and attack traffic for detector training, together with 15,000 labeled vulnerability records. On the vulnerability ranking test set, the decision tree achieves a precision of 91.8%, a recall of 89.5%, and an F1 score of 90.6%. On an independent test set containing 55,000 traffic samples, the combined detection pipeline achieves a precision of 94.5%, a recall of 88.3%, and an F1 score of 91.3%, while maintaining a false positive rate below 1.5%.

cs.CE↗
arXiv2026

Don't You Know, Pump it Up! Investigating Cryptocurrency Manipulation in Telegram-Driven Activity

Telegram plays a pivotal role in cryptocurrency communication and has been repeatedly associated with coordinated schemes, such as pump-and-dump manipulation. However, existing studies typically focus on known manipulation chats or a limited set of cryptocurrencies, leaving open the question of how Telegram is leveraged for mass promotional activity (shilling) at scale. Moving beyond these limitations, this work analyzes the interplay between information flows and market activity across public Telegram channels. To this end, we propose a scalable framework that (i) classifies crypto-related messages using a fine-tuned encoder model to filter semantic noise, (ii) detects anomalous spikes in cryptocurrency mentions via adaptive thresholding, and (iii) validates temporal associations between social bursts and market movements using quasi-experimental econometric methods (RDD and DiD). We apply this framework to one year of public Telegram data (14,499 channels and over 20 million messages) aligned with transaction data for more than 17,000 cryptocurrencies. Our analysis identifies 47 events consistent with potential pump-and-dump activity and 73 sustained market reactions, showing that manipulative signals are characterized by extreme temporal synchronization and precede price movements by seconds. Notably, psycholinguistic analysis reveals that pump-and-dump messages are linguistically indistinguishable from organic discussions, highlighting the limits of text-based detection alone. Finally, we estimate the cumulative financial volume of detected pump-and-dump events to exceed $200 million and release a public cryptocurrency dictionary and a fine-tuned classifier to support future research.

cs.SI↗
arXiv2026

Connectome-Based Modelling Reveals Orientation Maps in the Drosophila Optic Lobe

The ability to extract oriented edges from visual input is a core computation across animal vision systems. Orientation maps, long associated with the layered architecture of the mammalian visual cortex, systematically organise neurons by their preferred edge orientation. Despite lacking cortical structures, the Drosophila melanogaster brain contains feature-selective neurons and exhibits complex visual detection capacity, raising the question of whether map-like vision representations can emerge without cortical infrastructure. We integrate a complete fruit fly brain connectome with biologically grounded spiking neuron models to simulate neuroprocessing in the fly visual system. By driving the network with oriented stimuli and analysing downstream responses, we show that coherent orientation maps can emerge from purely connectome-constrained dynamics. These results suggest that species of independent origin could evolve similar visual structures.

cs.CE↗
arXiv2026

Risk-Sensitive Reward Composition for Conditional GFlowNets

Generative Flow Networks (GFlowNets) for structure-based drug design condition on one rigid protein structure. A flexible target holds several distinct structural shapes, its conformations, each occupied for a fraction of the simulation time. Scoring a candidate against all of them raises an open question: how do K scores become one reward? The designer cannot choose arbitrarily. Populations carry simulation error, and biology dictates which conformations are deal-breakers, so a candidate that fails one is disqualified, not merely ranked lower. No standard rule captures this. We compose the reward from a conditional value-at-risk (CVaR), a worst-case score rule, and an ambiguity radius expressing distrust in the stated weights. Together, these define a family of targets, amortised by a single conditional GFlowNet. We answer whether such a sampler can be trained on fully enumerable synthetic worlds, where every error is exact rather than estimated. Pricing the tail rather than averaging moves 2-10 times more mass to candidates that pass every conformation. One network covers the family to within 0.37-2.7x the error of a perfect sampler. An exact-KL oracle, a copy trained on the true target, shows if a shortfall is the optimiser's or the architecture's. When good candidates are rare, exploration decides: injecting unseen states finds 0.987-1.000 of good regions, while reweighting visited finds 0.35-0.76.

cs.CE↗
arXiv2026

Performance evaluation of variational quantum eigensolver and quantum dynamics algorithms on the advection-diffusion equation

Near-term quantum algorithms are a promising route to solving partial differential equations, but gauging their true potential requires separating algorithmic performance from sampling and hardware noise. We benchmark a ground-state variational quantum eigensolver (VQE), cast as a variational quantum linear solver, against the Trotterization, variational quantum imaginary time evolution, and adaptive variational quantum dynamics simulation methods applied to the one-dimensional advection-diffusion equation in the recent quantum-dynamics study by Alipanah et al. [Phys. Rev. Res. 7, 043318 (2025)] at matched grid and problem size. On a noiseless state-vector simulator the $N=4$ VQE drives the final-time infidelity to a numerical floor ($\sim\!10^{-14}$) once the depth reaches $L\approx5$, an \emph{algorithmic ceiling} set by exact expectation values. Evaluating the same solver with a finite number $S$ of measurement shots, still without hardware noise, makes the infidelity sampling limited, following $1-f\approx c/S$ (a best-case readout-sampling estimate, with the solution's signs assumed known), providing a regime-matched comparison with the shot-based emulator of Alipanah \emph{et al.}\ and explaining the gap to their noisy hardware runs ($>10^{-1}$). The benchmark thus decomposes the near-term error budget into algorithmic, sampling, and hardware contributions, with a matched-depth resource comparison. The formulation applies without modification across $N=4,5,6$ qubits and to a two-dimensional (eight-qubit, $16\times16$) problem evolved to $t=1$, where the state-vector VQE holds a $\sim\!10^{-7}$ algorithmic-ceiling infidelity against the sampling-limited $\sim\!10^{-5}$ of the corresponding shot-based simulation, a difference of measurement regime rather than algorithmic superiority.

quant-ph↗
arXiv2026

Aerodynamic Shape Design Space Exploration with Deep Latent Diffusion Model

We propose DiffGeo, a latent space diffusion-based generative framework for aerodynamic design space exploration under extreme data scarcity. DiffGeo combines a learned latent space model for automatic shape parameterization, with a diffusion sampler to directly generate novel, geometry-valid and controllable designs. We validate the approach on a series of case studies: (i) a 2D airfoil generation benchmark, where DiffGeo's latent diffusion model is compared against GAN- and VAE-based baselines in terms of sample quality, diversity and constraint adherence under limited data; (ii) integration into a surrogate-based optimization pipeline, where DiffGeo's conditional sampling produces task-informed airfoil data that improve both surrogate modeling and optimization performance; and (iii) extension to 3D turbomachinery blade prototyping, where DiffGeo generates realistic and high-performance blade geometries from a small set of reference designs. Throughout these investigations, DiffGeo achieves high-quality and diverse shape generation with at least an order of magnitude less data than alternatives, decouples geometry representation from design targets for flexible reuse, and seamlessly incorporates complex design constraints via energy-based conditioning. These capabilities demonstrate DiffGeo's potential to enhance early-stage design by automating design space exploration--improving efficiency, expanding design diversity and embedding engineering knowledge through controllable guidance.

cs.CE↗
arXiv2026

TxSum: User-Centered Ethereum Transaction Understanding with Micro-Level Semantic Grounding

Understanding the economic intent of Ethereum transactions is critical for user safety, yet current tools expose only raw on-chain data or surface-level intent, leading to widespread ``blind signing'' (approving transactions without understanding them). Through interviews with 16 Web3 users, we find that effective explanations should be structured, risk-aware, and grounded at the token-flow level. Motivated by these findings, we formulate TxSum, a new domain-grounded NLP task for DeFi transaction explanation, and construct a dataset of 187 complex Ethereum transactions with 2,375 token-flow annotations and transaction-level summaries. We further introduce MATEX, a grounded multi-agent framework for high-stakes transaction explanation. It selectively retrieves external knowledge under uncertainty and audits explanations against raw traces to improve token-flow-level factual consistency. MATEX achieves the strongest overall explanation quality, especially on micro-level factuality and intent quality. It improves user comprehension on complex transactions from 52.9% to 76.5% over the strongest baseline and raises malicious-transaction rejection from 36.0% to 88.0%, while maintaining a low false-rejection rate on benign transactions.

cs.CE↗