2 citations · 3 across the 7 of their papers we have counts for
7 papers
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…
A Framework for Large Scale Particle Filters Validated with Data Assimilation for Weather Simulation
Sebastian Friedemann, Kai Keller, Yen-Sen Lu +2
Particle filters are a group of algorithms to solve inverse problems through statistical Bayesian methods when the model does not comply with the linear and Gaussian hypothesis. Pa…
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…
Deep Surrogate for Direct Time Fluid Dynamics
Lucas Meyer, Louen Pottier, Alejandro Ribes +1
The ubiquity of fluids in the physical world explains the need to accurately simulate their dynamics for many scientific and engineering applications. Traditionally, well establish…
An elastic framework for ensemble-based large-scale data assimilation
Sebastian Friedemann, Bruno Raffin
Prediction of chaotic systems relies on a floating fusion of sensor data (observations) with a numerical model to decide on a good system trajectory and to compensate nonlinear fee…
Large scale in transit computation of quantiles for ensemble runs
Alejandro Ribes, Théophile Terraz, Bertrand Iooss +2
The classical approach for quantiles computation requires availability of the full sample before ranking it. In uncertainty quantification of numerical simulation models, this appr…