6 citations · 8 across the 8 of their papers we have counts for
8 papers
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, Éloi 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…
Physics-informed kernel learning
Nathan Doumèche, Francis Bach, Gérard Biau +1
Physics-informed machine learning typically integrates physical priors into the learning process by minimizing a loss function that includes both a data-driven term and a partial d…
Physics-informed machine learning as a kernel method
Nathan Doumèche, Francis Bach, Gérard Biau +1
Physics-informed machine learning combines the expressiveness of data-based approaches with the interpretability of physical models. In this context, we consider a general regressi…
Human spatial dynamics for electricity demand forecasting: the case of France during the 2022 energy crisis
Nathan Doumèche, Yann Allioux, Yannig Goude +1
Accurate electricity demand forecasting is crucial to meet energy security and efficiency, especially when relying on intermittent renewable energy sources. Recently, massive savin…
Forecasting Electric Vehicle Charging Station Occupancy: Smarter Mobility Data Challenge
Yvenn Amara-Ouali, Yannig Goude, Nathan Doumèche +10
The transport sector is a major contributor to greenhouse gas emissions in Europe. Shifting to electric vehicles (EVs) powered by a low-carbon energy mix would reduce carbon emissi…
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…