Utilising physics-guided deep learning to overcome data scarcity
Not on the map. Why?
Abstract (arXiv)
Deep learning (DL) relies heavily on data, and the quality of data influences its performance significantly. However, obtaining high-quality, well-annotated datasets can be challenging or even impossible in many real-world applications, such as structural risk estimation and medical diagnosis. This presents a significant barrier to the practical implementation of DL in these fields. Physics-guided deep learning (PGDL) is a novel type of DL that can integrate physics laws to train neural networks. This can be applied to any systems that are controlled or governed by physics laws, such as mechanics, finance and medical applications. It has been demonstrated that, with the additional information provided by physics laws, PGDL achieves great accuracy and generalisation in the presence of data scarcity. This review provides a detailed examination of PGDL and offers a structured overview of its use in addressing data scarcity across various fields, including physics, engineering and medical applications. Moreover, the review identifies the current limitations and opportunities for PGDL in relation to data scarcity and offers a thorough discussion on the future prospects of PGDL.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 40 of its 115 references that are in the map. First posted on arXiv 24 Nov 2022.
Builds on
40 of 115 references.
References · 40
- 2015Automatic differentiation in machine learning: a surveycited by 846
- 2020When and why PINNs fail to train: A neural tangent kernel perspectivecited by 629
- 2019Deep Learning for 3D Point Clouds: A Surveycited by 557
- 2019Machine Learning for Fluid Mechanicscited by 380
- 2020B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Datacited by 329
- 2020On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networkscited by 279
- 2019Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Datacited by 261
- 2020hp-VPINNs: Variational Physics-Informed Neural Networks With Domain Decompositioncited by 236
- 2017Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanicscited by 213
- 2020Machine Learning Force Fieldscited by 204
- 2015Cost Sensitive Learning of Deep Feature Representations from Imbalanced Datacited by 167
- 2021Exact imposition of boundary conditions with distance functions in physics-informed deep neural networkscited by 135
28 more
- 2018Adversarial Uncertainty Quantification in Physics-Informed Neural Networkscited by 120
- 2019An Energy Approach to the Solution of Partial Differential Equations in Computational Mechanics via Machine Learning: Concepts, Implementation and Applicationscited by 118
- 2019Transfer learning enhanced physics informed neural network for phase-field modeling of fracturecited by 116
- 2020Estimates on the generalization error of Physics Informed Neural Networks (PINNs) for approximating PDEscited by 115
- 2021Parallel Physics-Informed Neural Networks via Domain Decompositioncited by 112
- 2017DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamicscited by 79
- 2020Physics informed deep learning for computational elastodynamics without labeled datacited by 79
- 2020Local Extreme Learning Machines and Domain Decomposition for Solving Linear and Nonlinear Partial Differential Equationscited by 78
- 2019Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networkscited by 76
- 2020Physics-informed neural network for ultrasound nondestructive quantification of surface breaking crackscited by 67
- 2022Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systemscited by 54
- 2020Physics-Informed Neural Network for Modelling the Thermochemical Curing Process of Composite-Tool Systems During Manufacturecited by 48
- 2022Interfacing Finite Elements with Deep Neural Operators for Fast Multiscale Modeling of Mechanics Problemscited by 47
- 2021Physics informed neural networks for continuum micromechanicscited by 46
- 2020A Physics-Guided Neural Network Framework for Elastic Plates: Comparison of Governing Equations-Based and Energy-Based Approachescited by 34
- 2021The mixed deep energy method for resolving concentration features in finite strain hyperelasticitycited by 28
- 2020Meshless physics-informed deep learning method for three-dimensional solid mechanicscited by 27
- 2021Physics-informed neural network simulation of multiphase poroelasticity using stress-split sequential trainingcited by 26
- 2022Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks in Scientific Computingcited by 24
- 2020A nonlocal physics-informed deep learning framework using the peridynamic differential operatorcited by 21
- 2022EINNs: Epidemiologically-informed Neural Networkscited by 21
- 2020Physics-informed neural networks for myocardial perfusion MRI quantificationcited by 18
- 2020Improved unsupervised physics-informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patientscited by 16
- 2022Physics-informed neural networks to learn cardiac fiber orientation from multiple electroanatomical mapscited by 14
- 2021A Deep Collocation Method for the Bending Analysis of Kirchhoff Platecited by 13
- 2020Deep Autoencoder based Energy Method for the Bending, Vibration, and Buckling Analysis of Kirchhoff Platescited by 12
- 2020Physics-Informed Machine Learning Simulator for Wildfire Propagationcited by 6
- 2022An introduction to programming Physics-Informed Neural Network-based computational solid mechanicscited by 6
Built on by
Citing papers per year: 2023: 1.
1 paper.