mlsanityData to 14 Oct 2026

A Critical Review of Physics-Informed Machine Learning Applications in Subsurface Energy Systems

arXiv 2308.04457 ↗First posted 6 Aug 2023cs.LG stat.MLcited by 4

Not on the map. Why?

Abstract (arXiv)

Machine learning has emerged as a powerful tool in various fields, including computer vision, natural language processing, and speech recognition. It can unravel hidden patterns within large data sets and reveal unparalleled insights, revolutionizing many industries and disciplines. However, machine and deep learning models lack interpretability and limited domain-specific knowledge, especially in applications such as physics and engineering. Alternatively, physics-informed machine learning (PIML) techniques integrate physics principles into data-driven models. By combining deep learning with domain knowledge, PIML improves the generalization of the model, abidance by the governing physical laws, and interpretability. This paper comprehensively reviews PIML applications related to subsurface energy systems, mainly in the oil and gas industry. The review highlights the successful utilization of PIML for tasks such as seismic applications, reservoir simulation, hydrocarbons production forecasting, and intelligent decision-making in the exploration and production stages. Additionally, it demonstrates PIML's capabilities to revolutionize the oil and gas industry and other emerging areas of interest, such as carbon and hydrogen storage; and geothermal systems by providing more accurate and reliable predictions for resource management and operational efficiency.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 43 of its 183 references that are in the map. First posted on arXiv 6 Aug 2023.

Builds on

43 of 183 references.

References · 43

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
  4. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,611
  5. 2020Fourier Neural Operator for Parametric Partial Differential Equationscited by 2,908
  6. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,238
  7. 2019DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operatorscited by 2,018
  8. 2018Relational inductive biases, deep learning, and graph networkscited by 1,360
  9. 2018On the Spectral Bias of Neural Networkscited by 1,341
  10. 2019A Comprehensive Survey on Transfer Learningcited by 1,160
  11. 2021Characterizing possible failure modes in physics-informed neural networkscited by 643
  12. 2016Interaction Networks for Learning about Objects, Relations and Physicscited by 509
31 more
  1. 2020UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentationcited by 332
  2. 2021U-FNO -- An enhanced Fourier neural operator-based deep-learning model for multiphase flowcited by 297
  3. 2014Identifying and attacking the saddle point problem in high-dimensional non-convex optimizationcited by 289
  4. 2014The Loss Surfaces of Multilayer Networkscited by 280
  5. 2022Physical Inertial Poser (PIP): Physics-aware Real-time Human Motion Tracking from Sparse Inertial Sensorscited by 146
  6. 2020Understanding and mitigating gradient pathologies in physics-informed neural networkscited by 135
  7. 2020Descending through a Crowded Valley - Benchmarking Deep Learning Optimizerscited by 119
  8. 2019Physics-Informed Neural Networks for Multiphysics Data Assimilation with Application to Subsurface Transportcited by 61
  9. 2021The Old and the New: Can Physics-Informed Deep-Learning Replace Traditional Linear Solvers?cited by 61
  10. 2021Seismic wave propagation and inversion with Neural Operatorscited by 51
  11. 2021Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functionscited by 49
  12. 2023Fourier-MIONet: Fourier-enhanced multiple-input neural operators for multiphase modeling of geological carbon sequestrationcited by 46
  13. 2018A Progressive Batching L-BFGS Method for Machine Learningcited by 45
  14. 2022Visual Attention Methods in Deep Learning: An In-Depth Surveycited by 37
  15. 2016A Multi-Batch L-BFGS Method for Machine Learningcited by 31
  16. 2019Physics-Guided Architecture (PGA) of Neural Networks for Quantifying Uncertainty in Lake Temperature Modelingcited by 26
  17. 2021Physics-informed neural network simulation of multiphase poroelasticity using stress-split sequential trainingcited by 26
  18. 2022Physics-Informed Neural Networks with Adaptive Localized Artificial Viscositycited by 22
  19. 2014On the saddle point problem for non-convex optimizationcited by 20
  20. 2021PINNup: Robust neural network wavefield solutions using frequency upscaling and neuron splittingcited by 20
  21. 2022Solving Seismic Wave Equations on Variable Velocity Models with Fourier Neural Operatorcited by 18
  22. 2017First-order Methods Almost Always Avoid Saddle Pointscited by 14
  23. 2019Is it Time to Swish? Comparing Deep Learning Activation Functions Across NLP taskscited by 9
  24. 2021A Gradient-based Deep Neural Network Model for Simulating Multiphase Flow in Porous Mediacited by 8
  25. 2022Physics-Informed Graph Neural Network for Spatial-temporal Production Forecastingcited by 8
  26. 2021Physics-informed attention-based neural network for solving non-linear partial differential equationscited by 6
  27. 2021A Physics-Constrained Deep Learning Model for Simulating Multiphase Flow in 3D Heterogeneous Porous Mediacited by 6
  28. 2021Estimating permeability of 3D micro-CT images by physics-informed CNNs based on DNScited by 5
  29. 2022Uncertainty Quantification for Transport in Porous media using Parameterized Physics Informed neural Networkscited by 2
  30. 2023Learning Generic Solutions for Multiphase Transport in Porous Media via the Flux Functions Operatorcited by 2
  31. 2023Simulation and Prediction of Countercurrent Spontaneous Imbibition at Early and Late Times Using Physics-Informed Neural Networkscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 1.

4 papers.

  1. 2025The Transparent Earth: A Multimodal Foundation Model for the Earth's Subsurfacecited by 1
  2. 2024Evaluating the Stability of Deep Learning Latent Feature Spacescited by 0
  3. 2025Unlocking the Invisible Urban Traffic Dynamics under Extreme Weather: A New Physics-Constrained Hamiltonian Learning Algorithmcited by 0
  4. 2026Toward Artificial Intelligence Enabled Earth System Couplingcited by 0