Torch-DPhys: A Torch-Native, MOOSE-Compatible Framework for Differentiable Physics Simulation and Hybrid Machine Learning Integration
We present Torch-DPhys, a Torch-native framework implemented in PyTorch for differentiable physics simulation and hybrid physics-machine learning. It implements selected thermal-hydraulic modeling concepts and discretizations from MOOSE's Thermal Hydraulics Module and the System Analysis Module (SAM), with a preliminary extension to continuum mechanics. Native tensor residuals support CPU and GPU execution, compilation of selected numerical kernels, and sparse direct linear solves. Sensitivities are obtained by differentiating executed numerical operations or through discrete adjoints of converged residual equations. Verification combines analytical solutions, reference-code comparisons, ...
| Authors | Akzhol Almukhametov, Yang Liu |
|---|---|
| Primary category | cs.LG |
| Published | 16 October 2026 |
| Last updated at source | 16 October 2026 |
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