4 papers
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…
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…
Physics-Constrained Flow Matching: Sampling Generative Models with Hard Constraints
Utkarsh Utkarsh, Pengfei Cai, Alan Edelman +2
Deep generative models have recently been applied to physical systems governed by partial differential equations (PDEs), offering scalable simulation and uncertainty-aware inferenc…
Univariate Conditional Variational Autoencoder for Morphogenic Patterns Design in Frontal Polymerization-Based Manufacturing
Qibang Liu, Pengfei Cai, Diab Abueidda +4
Under some initial and boundary conditions, the rapid reaction-thermal diffusion process taking place during frontal polymerization (FP) destabilizes the planar mode of front propa…