1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2026★ 1 cited
PLAID: A Unified Data Model for Machine Learning on Heterogeneous Physics Simulations
Fabien Casenave, Xavier Roynard, Brian Staber +17
Machine learning-based surrogate models have emerged as a powerful tool to accelerate simulation-driven scientific workflows, but their adoption is limited by the lack of large-sca…
cs.CE2026
An End-to-End PyTorch Interface for Differentiable PDE Solvers: A RANS Model-Correction Study
Luca Saverio, Michele Alessandro Bucci, Gianmarco Farro +2
This work presents an end-to-end strategy for solving inverse problems constrained by Partial Differential Equations within a fully differentiable Machine Learning framework. The p…