Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications

• URL: http://arxiv.org/abs/2211.08064v1
• Date: Tue, 15 Nov 2022 11:34:30 GMT
• Title: Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications
• Authors: Zhongkai Hao, Songming Liu, Yichi Zhang, Chengyang Ying, Yao Feng, Hang Su, Jun Zhu
• Abstract summary: Recent work shows that it provides potential benefits for machine learning models by incorporating the physical prior and collected data. We present this learning paradigm called Physics-Informed Machine Learning (PIML) which is to build a model that leverages empirical data and available physical prior knowledge.
• Score: 31.157298426186653
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances of data-driven machine learning have revolutionized fields like computer vision, reinforcement learning, and many scientific and engineering domains. In many real-world and scientific problems, systems that generate data are governed by physical laws. Recent work shows that it provides potential benefits for machine learning models by incorporating the physical prior and collected data, which makes the intersection of machine learning and physics become a prevailing paradigm. In this survey, we present this learning paradigm called Physics-Informed Machine Learning (PIML) which is to build a model that leverages empirical data and available physical prior knowledge to improve performance on a set of tasks that involve a physical mechanism. We systematically review the recent development of physics-informed machine learning from three perspectives of machine learning tasks, representation of physical prior, and methods for incorporating physical prior. We also propose several important open research problems based on the current trends in the field. We argue that encoding different forms of physical prior into model architectures, optimizers, inference algorithms, and significant domain-specific applications like inverse engineering design and robotic control is far from fully being explored in the field of physics-informed machine learning. We believe that this study will encourage researchers in the machine learning community to actively participate in the interdisciplinary research of physics-informed machine learning.

Related papers

• Machine Learning with Physics Knowledge for Prediction: A Survey [16.96920919164813]
  This survey examines the broad suite of methods and models for combining machine learning with physics knowledge for prediction and forecast. The survey has two parts. The first considers incorporating physics knowledge on an architectural level through objective functions, structured predictive models, and data augmentation. The second considers data as physics knowledge, which motivates looking at multi-task, meta, and contextual learning as an alternative approach to incorporating physics knowledge in a data-driven fashion.
  arXiv  Detail & Related papers  (2024-08-19T09:36:07Z)
• 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)
• Physics-Informed Computer Vision: A Review and Perspectives [22.71741766133866]
  incorporation of physical information in machine learning frameworks is opening and transforming many application domains. We present a systematic literature review of more than 250 papers on formulation and approaches to computer vision tasks guided by physical laws.
  arXiv  Detail & Related papers  (2023-05-29T11:55:11Z)
• Advancing Reacting Flow Simulations with Data-Driven Models [50.9598607067535]
  Key to effective use of machine learning tools in multi-physics problems is to couple them to physical and computer models. The present chapter reviews some of the open opportunities for the application of data-driven reduced-order modeling of combustion systems.
  arXiv  Detail & Related papers  (2022-09-05T16:48:34Z)
• Symmetry Group Equivariant Architectures for Physics [52.784926970374556]
  In the domain of machine learning, an awareness of symmetries has driven impressive performance breakthroughs. We argue that both the physics community and the broader machine learning community have much to understand.
  arXiv  Detail & Related papers  (2022-03-11T18:27:04Z)
• Which priors matter? Benchmarking models for learning latent dynamics [70.88999063639146]
  Several methods have proposed to integrate priors from classical mechanics into machine learning models. We take a sober look at the current capabilities of these models. We find that the use of continuous and time-reversible dynamics benefits models of all classes.
  arXiv  Detail & Related papers  (2021-11-09T23:48:21Z)
• Constructing Neural Network-Based Models for Simulating Dynamical Systems [59.0861954179401]
  Data-driven modeling is an alternative paradigm that seeks to learn an approximation of the dynamics of a system using observations of the true system. This paper provides a survey of the different ways to construct models of dynamical systems using neural networks. In addition to the basic overview, we review the related literature and outline the most significant challenges from numerical simulations that this modeling paradigm must overcome.
  arXiv  Detail & Related papers  (2021-11-02T10:51:42Z)
• Measuring and modeling the motor system with machine learning [117.44028458220427]
  The utility of machine learning in understanding the motor system is promising a revolution in how to collect, measure, and analyze data. We discuss the growing use of machine learning: from pose estimation, kinematic analyses, dimensionality reduction, and closed-loop feedback, to its use in understanding neural correlates and untangling sensorimotor systems.
  arXiv  Detail & Related papers  (2021-03-22T12:42:16Z)
• Knowledge as Invariance -- History and Perspectives of Knowledge-augmented Machine Learning [69.99522650448213]
  Research in machine learning is at a turning point. Research interests are shifting away from increasing the performance of highly parameterized models to exceedingly specific tasks. This white paper provides an introduction and discussion of this emerging field in machine learning research.
  arXiv  Detail & Related papers  (2020-12-21T15:07:19Z)
• Integrating Machine Learning with Physics-Based Modeling [17.392391163553334]
  This article focuses on one particular issue of broad interest: How can we integrate machine learning with physics-based modeling? We discuss the two most important issues for developing machine learning-based physical models: Imposing physical constraints and obtaining optimal datasets. We end with a general discussion on where this integration will lead us, and where the new frontier will be after machine learning is successfully integrated into scientific modeling.
  arXiv  Detail & Related papers  (2020-06-04T02:35:10Z)
• Physical reservoir computing -- An introductory perspective [0.0]
  Physical reservoir computing allows one to exploit the complex dynamics of physical systems as information-processing devices. This paper aims to illustrate the potentials of the framework using examples from soft robotics.
  arXiv  Detail & Related papers  (2020-05-03T05:39:06Z)

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.