1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2025★ 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.LG2023
Packed-Ensemble Surrogate Models for Fluid Flow Estimation Arround Airfoil Geometries
Anthony Kalaydjian, Anton Balykov, Alexi Semiz +1
Physical based simulations can be very time and computationally demanding tasks. One way of accelerating these processes is by making use of data-driven surrogate models that learn…