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Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications
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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. By integrating the data and mathematical physics models seamlessly, it can guide the machine learning model towards solutions that are physically plausible, improving accuracy and efficiency even in uncertain and high-dimensional contexts. 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 being fully explored in the field of physics-informed machine learning. We believe that the interdisciplinary research of physics-informed machine learning will significantly propel research progress, foster the creation of more effective machine learning models, and also offer invaluable assistance in addressing long-standing problems in related disciplines.
Forward citations
Cited by 8 Pith papers
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Continual-Learning Physics-Informed Neural Networks for Parameterized Partial Differential Equations
A continual-learning training scheme with Bayesian task selection, dynamic weighting, and sparse physics replay improves accuracy and query efficiency of parameterized physics-informed neural networks on five benchmarks.
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Experimental cross sections for K-shell ionization by electron impact
A new database compiles 2509 experimental K-shell ionization cross sections for 65 elements, with analysis of coverage and experimental methods.
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Physics-Infused Reduced-Order Modeling for Analysis of Multi-Layered Hypersonic Thermal Protection Systems
PIROM couples a lumped-capacitance heat model with Mori-Zwanzig-derived hidden-state corrections and generalizes to unseen heat flux and material perturbations with roughly 1% NRMSE at 100x speedup.
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IKEBANA: A Neural-Network approach for the K-shell ionization by electron impact
IKEBANA is a neural network that reproduces experimental K-shell electron-impact ionization cross sections from atomic number and overvoltage, from H to U.
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Autonomous Task Completion Based on Goal-directed Answer Set Programming
An early-stage logic-programming planner using s(CASP) and dependency graph pruning is reported to cut task planning time from hours to under a second in a small simulated environment.
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Toward Knowledge-Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human-AI Synergy
A perspective arguing that inverse design in manufacturing improves when expert-guided problem definition, physics-informed ML, and LLM interfaces are combined.
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Cardiovascular Digital Twins from Physics Based to Data Driven Approaches
A review of cardiovascular digital twin modeling paradigms that concludes hybrid physics-informed and graph-based methods are the most promising direction for clinical deployment.
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Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review
This review organizes machine learning and physics-informed neural network applications to semiconductor film deposition into four categories and outlines future research directions.
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