FLASH-MAX embeds exact Maxwell solutions as neurons in a neural network to reconstruct homogeneous EM fields from sparse data with guaranteed zero PDE residual and proven universal approximation on arbitrary domains.
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SCICONVBENCH is a new benchmark evaluating LLMs on multi-turn disambiguation and inconsistency resolution for task formulation in computational science, with frontier models reaching only 52.7% success on fluid mechanics disambiguation cases.
A prox-based semi-smooth Newton method for TV-minimization that is globally well-posed and locally superlinearly convergent under finite element discretization, extending to broader convex problems.
Conditional flow matching learns a velocity field to sample from measurement-conditioned posteriors in physics inverse problems, with early stopping to prevent variance collapse and selective memorization under finite training data.
ALL-FEM fine-tunes LLMs on a corpus of verified FEniCS scripts and uses multi-agent workflows to automate finite element code generation, achieving 71.79% success on 39 benchmarks across elasticity, flow, and coupled problems.
A multi-agent LLM framework autonomously completes the full computational mechanics pipeline from a photograph to a code-compliant engineering report on a steel L-bracket example.
Metriplectic systems converge to entropy extrema at fixed Hamiltonian under stated conditions; a Landau-inspired class reduces the check to two simpler conditions for use in equilibrium relaxation schemes.
A heat and mass transfer model for fabric drying with heated cylinders is proposed, with parameters estimated by nonlinear least squares from company data to predict drying time and residual moisture.
citing papers explorer
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Fast Reconstruction of Exact Maxwell Dynamics from Sparse Data
FLASH-MAX embeds exact Maxwell solutions as neurons in a neural network to reconstruct homogeneous EM fields from sparse data with guaranteed zero PDE residual and proven universal approximation on arbitrary domains.
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SCICONVBENCH: Benchmarking LLMs on Multi-Turn Clarification for Task Formulation in Computational Science
SCICONVBENCH is a new benchmark evaluating LLMs on multi-turn disambiguation and inconsistency resolution for task formulation in computational science, with frontier models reaching only 52.7% success on fluid mechanics disambiguation cases.
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A $\operatorname{prox}$-Based Semi-Smooth Newton Method for TV-Minimization
A prox-based semi-smooth Newton method for TV-minimization that is globally well-posed and locally superlinearly convergent under finite element discretization, extending to broader convex problems.
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Conditional flow matching for physics-constrained inverse problems with finite training data
Conditional flow matching learns a velocity field to sample from measurement-conditioned posteriors in physics inverse problems, with early stopping to prevent variance collapse and selective memorization under finite training data.
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ALL-FEM: Agentic Large Language models Fine-tuned for Finite Element Methods
ALL-FEM fine-tunes LLMs on a corpus of verified FEniCS scripts and uses multi-agent workflows to automate finite element code generation, achieving 71.79% success on 39 benchmarks across elasticity, flow, and coupled problems.
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From Perception to Autonomous Computational Modeling: A Multi-Agent Approach
A multi-agent LLM framework autonomously completes the full computational mechanics pipeline from a photograph to a code-compliant engineering report on a steel L-bracket example.
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Metriplectic relaxation to equilibria
Metriplectic systems converge to entropy extrema at fixed Hamiltonian under stated conditions; a Landau-inspired class reduces the check to two simpler conditions for use in equilibrium relaxation schemes.
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Heat and mass transfer through fabric: a model for fabric drying with heated cylinders
A heat and mass transfer model for fabric drying with heated cylinders is proposed, with parameters estimated by nonlinear least squares from company data to predict drying time and residual moisture.