4 papers
A Fast Learning-Based Surrogate of Electrical Machines using a Reduced Basis
Alejandro Ribés, Nawfal Benchekroun, Théo Delagnes
A surrogate model approximates the outputs of a solver of Partial Differential Equations (PDEs) with a low computational cost. In this article, we propose a method to build learnin…
High Throughput Training of Deep Surrogates from Large Ensemble Runs
Lucas Meyer, Marc Schouler, Robert Alexander Caulk +2
Recent years have seen a surge in deep learning approaches to accelerate numerical solvers, which provide faithful but computationally intensive simulations of the physical world.…
Training Deep Surrogate Models with Large Scale Online Learning
Lucas Meyer, Marc Schouler, Robert Alexander Caulk +2
The spatiotemporal resolution of Partial Differential Equations (PDEs) plays important roles in the mathematical description of the world's physical phenomena. In general, scientis…
Simulation-Based Parallel Training
Lucas Meyer, Alejandro Ribés, Bruno Raffin
Numerical simulations are ubiquitous in science and engineering. Machine learning for science investigates how artificial neural architectures can learn from these simulations to s…