An Overview on Machine Learning Methods for Partial Differential Equations: from Physics Informed Neural Networks to Deep Operator Learning
<|reference_start|>An Overview on Machine Learning Methods for Partial Differential Equations: from Physics Informed Neural Networks to Deep Operator Learning: The approximation of solutions of partial differential equations (PDEs) with numerical algorithms is a central topic in applied mathematics. For many decades, various types of methods for this purpose have been developed and extensively studied. One class of methods which has received a lot of attention in recent years are machine learning-based methods, which typically involve the training of artificial neural networks (ANNs) by means of stochastic gradient descent type optimization methods. While approximation methods for PDEs using ANNs have first been proposed in the 1990s they have only gained wide popularity in the last decade with the rise of deep learning. This article aims to provide an introduction to some of these methods and the mathematical theory on which they are based. We discuss methods such as physics-informed neural networks (PINNs) and deep BSDE methods and consider several operator learning approaches.<|reference_end|>
@article{gonon2024an,
    title={An Overview on Machine Learning Methods for Partial Differential
  Equations: from Physics Informed Neural Networks to Deep Operator Learning},
    author={Lukas Gonon, Arnulf Jentzen, Benno Kuckuck, Siyu Liang, Adrian
  Riekert, Philippe von Wurstemberger},
    journal={arXiv preprint arXiv:2408.13222},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.13222},
    primaryClass={math.NA cs.NA stat.ML}
}