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cs.LG2026
Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards
Pengfei Cai, Utkarsh Utkarsh, Alan Edelman +2
Partial differential equations (PDEs) are foundational to modeling in science and engineering, but constructing reliable numerical solvers remains labor-intensive, demanding expert…
cs.LG2026
SNAP-FM: Sparse Nonlinear Accelerated Projection for Physics-Constrained Generative Modeling
Alaina Kolli, Theodoros Xenakis, Utkarsh Utkarsh +4
Generative models have emerged as scalable surrogates for physical simulation, yet they offer no guarantee that their outputs respect the conservation laws, boundary conditions, an…
cs.LG2025
Scientific Machine Learning of Chaotic Systems Learns Reduced-Order Equations for Neural Populations
Anthony G. Chesebro, David Hofmann, Vaibhav Dixit +6
Extracting interpretable mathematical models from complex dynamical systems is difficult, especially for chaotic dynamics observed with noisy experimental data. We present PEM-UDE,…