Physics-informed machine learning: A mathematical framework with applications to time series forecasting

TL;DR

This paper develops a mathematical framework for physics-informed machine learning, analyzing its statistical properties, and demonstrates its application to time series forecasting in energy and tourism sectors.

Contribution

It introduces a kernel-based formulation of PIML, enabling new algorithms and efficient GPU implementation, and applies these methods to real-world forecasting problems.

Findings

PINNs analyzed for approximation, consistency, overfitting, convergence

Kernel methods provide new insights and algorithms for PIML

Physics-constrained time series forecasting improves accuracy

Abstract

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 partial differential equation (PDE) system that the regression function must satisfy. In the first part of this dissertation, we analyze the statistical properties of PIML methods. In particular, we study the properties of physics-informed neural networks (PINNs) in terms of approximation, consistency, overfitting, and convergence. We then show how PIML problems can be framed as kernel methods, making it possible to apply the tools of kernel ridge regression to better understand their behavior. In addition, we use this kernel formulation to develop novel physics-informed algorithms and implement them efficiently on GPUs. The second part explores industrial applications in forecasting energy signals during atypical periods. We present results from the Smarter Mobility challenge on electric vehicle charging occupancy and examine the impact of mobility on electricity demand. Finally, we introduce a physics-constrained framework for designing and enforcing constraints in time series, applying it to load forecasting and tourism forecasting in various countries.