4 papers
On the convergence of PINNs
Nathan Doumèche, Gérard Biau, Claire Boyer
Physics-informed neural networks (PINNs) are a promising approach that combines the power of neural networks with the interpretability of physical modeling. PINNs have shown good p…
Fast kernel methods: Sobolev, physics-informed, and additive models
Nathan Doumèche, Francis Bach, Gérard Biau +1
Kernel methods are powerful tools in statistical learning, but their cubic complexity in the sample size n limits their use on large-scale datasets. In this work, we introduce a sc…
Physics-informed machine learning: A mathematical framework with applications to time series forecasting
Nathan Doumèche
Physics-informed machine learning (PIML) is an emerging framework that integrates physical knowledge into machine learning models. This physical prior often takes the form of a par…
Forecasting time series with constraints
Nathan Doumèche, Francis Bach, Ãloi Bedek +3
Time series forecasting presents unique challenges that limit the effectiveness of traditional machine learning algorithms. To address these limitations, various approaches have in…