16 citations · 16 across the 3 of their papers we have counts for
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…
Automated Translation and Accelerated Solving of Differential Equations on Multiple GPU Platforms
Utkarsh Utkarsh, Valentin Churavy, Yingbo Ma +8
We demonstrate a high-performance vendor-agnostic method for massively parallel solving of ensembles of ordinary differential equations (ODEs) and stochastic differential equations…