When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning

• URL: http://arxiv.org/abs/2203.16797v1
• Date: Thu, 31 Mar 2022 04:58:27 GMT
• Title: When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning
• Authors: Chuizheng Meng, Sungyong Seo, Defu Cao, Sam Griesemer, Yan Liu
• Abstract summary: Physics-informed machine learning (PIML) is an effective way to mitigate the shortage of training data, increase models' generalizability and to ensure the physical plausibility of results. We survey an abundant number of recent works in PIML and summarize them from three aspects: (1) motivations of PIML, (2) physics knowledge in PIML, and (3) methods of physics knowledge integration in PIML.
• Score: 14.296078151381591
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Physics-informed machine learning (PIML), referring to the combination of prior knowledge of physics, which is the high level abstraction of natural phenomenons and human behaviours in the long history, with data-driven machine learning models, has emerged as an effective way to mitigate the shortage of training data, to increase models' generalizability and to ensure the physical plausibility of results. In this paper, we survey an abundant number of recent works in PIML and summarize them from three aspects: (1) motivations of PIML, (2) physics knowledge in PIML, (3) methods of physics knowledge integration in PIML. We also discuss current challenges and corresponding research opportunities in PIML.

Related papers

• Recent Advances on Machine Learning for Computational Fluid Dynamics: A Survey [51.87875066383221]
  This paper introduces fundamental concepts, traditional methods, and benchmark datasets, then examine the various roles Machine Learning plays in improving CFD. We highlight real-world applications of ML for CFD in critical scientific and engineering disciplines, including aerodynamics, combustion, atmosphere & ocean science, biology fluid, plasma, symbolic regression, and reduced order modeling. We draw the conclusion that ML is poised to significantly transform CFD research by enhancing simulation accuracy, reducing computational time, and enabling more complex analyses of fluid dynamics.
  arXiv  Detail & Related papers  (2024-08-22T07:33:11Z)
• Physics-Informed Machine Learning for Smart Additive Manufacturing [2.3091320511105353]
  This paper focuses on the development of a physics-informed machine learning (PIML) model by integrating neural networks and physical laws to improve model accuracy, transparency, and generalization with case studies in laser metal deposition (LMD)
  arXiv  Detail & Related papers  (2024-07-15T14:40:24Z)
• Improving Molecular Modeling with Geometric GNNs: an Empirical Study [56.52346265722167]
  This paper focuses on the impact of different canonicalization methods, (2) graph creation strategies, and (3) auxiliary tasks, on performance, scalability and symmetry enforcement. Our findings and insights aim to guide researchers in selecting optimal modeling components for molecular modeling tasks.
  arXiv  Detail & Related papers  (2024-07-11T09:04:12Z)
• Physics-Enhanced Machine Learning: a position paper for dynamical systems investigations [0.0]
  Physics-Enhanced Machine Learning (PEML) is also known as Scientific Machine Learning. Three broad groups of PEML approaches are discussed: physics-guided, physics-encoded and physics-informed. The advantages and challenges in developing PEML strategies for guiding high-consequence decision making in engineering applications involving complex dynamical systems are presented.
  arXiv  Detail & Related papers  (2024-05-08T14:15:51Z)
• Physics-Informed Machine Learning On Polar Ice: A Survey [0.6827423171182154]
  The mass loss of the polar ice sheets contributes to ongoing sea-level rise and changing ocean circulation. Traditional physical models can guarantee physically meaningful results, but they have limitations in producing high-resolution results. Data-driven approaches require large amounts of high-quality and labeled data, which is rarely available in the polar regions. As a promising framework that leverages the advantages of physical models and data-driven methods, physics-informed machine learning (PIML) has been widely studied in recent years.
  arXiv  Detail & Related papers  (2024-04-30T13:12:36Z)
• Discussing the Spectrum of Physics-Enhanced Machine Learning; a Survey on Structural Mechanics Applications [3.430730454702436]
  The intersection of physics and machine learning has given rise to the physics-enhanced machine learning (PEML) paradigm. PEML aims to improve the capabilities and reduce the individual shortcomings of data- or physics-only methods. As a contribution, this paper underscores the significance of PEML in pushing the boundaries of scientific and engineering research.
  arXiv  Detail & Related papers  (2023-10-31T12:50:25Z)
• Discovering Interpretable Physical Models using Symbolic Regression and Discrete Exterior Calculus [55.2480439325792]
  We propose a framework that combines Symbolic Regression (SR) and Discrete Exterior Calculus (DEC) for the automated discovery of physical models. DEC provides building blocks for the discrete analogue of field theories, which are beyond the state-of-the-art applications of SR to physical problems. We prove the effectiveness of our methodology by re-discovering three models of Continuum Physics from synthetic experimental data.
  arXiv  Detail & Related papers  (2023-10-10T13:23:05Z)
• Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems [0.0]
  Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints. The basic premise of PIML is that the integration of ML and physics can yield more effective, physically consistent, and data-efficient models. This paper aims to provide a tutorial-like overview of the recent advances in PIML for dynamical system modeling and control.
  arXiv  Detail & Related papers  (2023-06-24T05:24:48Z)
• Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics [77.34726150561087]
  Recent developments in artificial neural networks, particularly deep learning (DL), are reviewed in detail. Both hybrid and pure machine learning (ML) methods are discussed. History and limitations of AI are recounted and discussed, with particular attention at pointing out misstatements or misconceptions of the classics.
  arXiv  Detail & Related papers  (2022-12-18T02:03:00Z)
• Machine Learning Force Fields [54.48599172620472]
  Machine Learning (ML) has enabled numerous advances in computational chemistry. One of the most promising applications is the construction of ML-based force fields (FFs) This review gives an overview of applications of ML-FFs and the chemical insights that can be obtained from them.
  arXiv  Detail & Related papers  (2020-10-14T13:14:14Z)
• Machine Learning in Nano-Scale Biomedical Engineering [77.75587007080894]
  We review the existing research regarding the use of machine learning in nano-scale biomedical engineering. The main challenges that can be formulated as ML problems are classified into the three main categories. For each of the presented methodologies, special emphasis is given to its principles, applications, and limitations.
  arXiv  Detail & Related papers  (2020-08-05T15:45:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.