Article | REF: IN703 V1

Physics-inspired machine learning - Principles and application to solar energy forecasting

Author: Vincent LE GUEN

Publication date: December 10, 2023

You do not have access to this resource.
Click here to request your free trial access!

Already subscribed? Log in!


Overview

Français

ABSTRACT

This article deals with physics-informed machine learning, which is a technique leveraging prior physical knowledge and data to describe physical processes. These hybrid models enable to accelerate numerical simulations, to be more data efficient when learning, to provide more interpretable predictions compliant with physical laws that generalize better. This article also presents an industrial application at EDF on short-term solar energy forecasting based on ground-based cameras.

Read this article from a comprehensive knowledge base, updated and supplemented with articles reviewed by scientific committees.

Read the article

AUTHOR

  • Vincent LE GUEN: Researcher - EDF R&D, Chatou - SINCLAIR AI Lab, Palaiseau

 INTRODUCTION

The development of computer science and artificial intelligence (AI) has profoundly changed scientific research over the course of the 20th century. The traditional paradigm of theoretical equations and experimental validation has been supported by the use of numerical simulation, which has become essential for analyzing complex systems in physics, engineering, biology and other fields.

With the automation of experiments and the exponential increase in the number of sensors, a deluge of observational data is now available. To extract relevant information from this data and fuel scientific discovery, statistical learning (machine learning), particularly deep learning, is particularly attractive. Over the past decade, deep learning has enabled spectacular progress in the fields of computer vision, natural language processing and gaming, thanks to its ability to extract complex non-linear relationships from massive data and build predictive models. In the industrial field, these AI methods open the way to the development of digital twins, enabling complex systems to be simulated much more rapidly than with traditional solving techniques, and results to be extrapolated to new configurations.

However, deep learning methods require very large, high-quality labeled databases for training, which is not always possible for some very expensive scientific experiments. In addition, purely data-driven methods are often regarded as poorly explainable black boxes, suffer from generalization problems outside their training domain and can produce physically inconsistent predictions.

Introducing physical knowledge into learning methods is a very promising way of solving these problems. Physics-inspired machine learning can be defined as a paradigm aimed at building models that exploit both observational data and a priori physical knowledge to solve tasks that are based on an underlying physical process. These ideas of hybridization are quite old, but have seen a strong revival with the successes of modern deep learning.

This article reviews current knowledge on methods for hybridizing statistical learning and a priori knowledge, focusing on the study of physical phenomena. The main hybridization strategies are presented, and the advantages and fields of application of these methods are discussed. The article also presents an industrial application at EDF for forecasting photovoltaic production from ground cameras, for which a hybrid deep learning model has been developed. Finally, the article looks at the main scientific and industrial challenges ahead for these methods.

You do not have access to this resource.

Exclusive to subscribers. 97% yet to be discovered!

You do not have access to this resource.
Click here to request your free trial access!

Already subscribed? Log in!


The Ultimate Scientific and Technical Reference

A Comprehensive Knowledge Base, with over 1,200 authors and 100 scientific advisors
+ More than 10,000 articles and 1,000 how-to sheets, over 800 new or updated articles every year
From design to prototyping, right through to industrialization, the reference for securing the development of your industrial projects

KEYWORDS

physics   |   deep learning   |   machine learning   |   hybrid models


This article is included in

Software technologies and System architectures

This offer includes:

Knowledge Base

Updated and enriched with articles validated by our scientific committees

Services

A set of exclusive tools to complement the resources

Practical Path

Operational and didactic, to guarantee the acquisition of transversal skills

Doc & Quiz

Interactive articles with quizzes, for constructive reading

Subscribe now!

Ongoing reading
Physics-inspired statistical learning