mlsanityData to 7 Oct 2026

Inverse modeling of nonisothermal multiphase poromechanics using physics-informed neural networks

arXiv 2209.03276 ↗First posted 7 Sep 2022cs.LG cs.CEcited by 6

Not on the map. Why?

Abstract (arXiv)

We propose a solution strategy for parameter identification in multiphase thermo-hydro-mechanical (THM) processes in porous media using physics-informed neural networks (PINNs). We employ a dimensionless form of the THM governing equations that is particularly well suited for the inverse problem, and we leverage the sequential multiphysics PINN solver we developed in previous work. We validate the proposed inverse-modeling approach on multiple benchmark problems, including Terzaghi's isothermal consolidation problem, Barry-Mercer's isothermal injection-production problem, and nonisothermal consolidation of an unsaturated soil layer. We report the excellent performance of the proposed sequential PINN-THM inverse solver, thus paving the way for the application of PINNs to inverse modeling of complex nonlinear multiphysics problems.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 19 of its 87 references that are in the map. First posted on arXiv 7 Sep 2022.

Builds on

19 of 87 references.

References · 19

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2015Automatic differentiation in machine learning: a surveycited by 846
  3. 2017GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networkscited by 816
  4. 2020When and why PINNs fail to train: A neural tangent kernel perspectivecited by 629
  5. 2022Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's nextcited by 603
  6. 2021Physics-informed neural networks (PINNs) for fluid mechanics: A reviewcited by 470
  7. 2022Respecting causality is all you need for training physics-informed neural networkscited by 154
  8. 2020Understanding and mitigating gradient pathologies in physics-informed neural networkscited by 134
  9. 2020Physics informed deep learning for computational elastodynamics without labeled datacited by 79
  10. 2020Physics-informed deep learning for incompressible laminar flowscited by 54
  11. 2020Physics-Informed Neural Network for Modelling the Thermochemical Curing Process of Composite-Tool Systems During Manufacturecited by 48
  12. 2014Likelihood-informed dimension reduction for nonlinear inverse problemscited by 26
7 more
  1. 2021Physics-informed neural network simulation of multiphase poroelasticity using stress-split sequential trainingcited by 26
  2. 2020Physics-informed Neural Networks for Solving Nonlinear Diffusivity and Biot's equationscited by 20
  3. 2020Deep Learning for One-dimensional Consolidationcited by 14
  4. 2020Physics Informed Deep Learning for Transport in Porous Media. Buckley Leverett Problemcited by 10
  5. 2022Physics-informed neural network solution of thermo-hydro-mechanical (THM) processes in porous mediacited by 8
  6. 2020Bayesian Poroelastic Aquifer Characterization from InSAR Surface Deformation Data. Part I: Maximum A Posteriori Estimatecited by 5
  7. 2020Physics-informed deep learning for flow and deformation in poroelastic mediacited by 5

Built on by

Cited by year

Citing papers per year: 2024: 4; 2025: 2.

6 papers.

  1. 2024From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learningcited by 73
  2. 2025Physics-Informed DeepONet Coupled with FEM for Convective Transport in Porous Media with Sharp Gaussian Sourcescited by 3
  3. 2024Physics-informed Deep Learning to Solve Three-dimensional Terzaghi Consolidation Equation: Forward and Inverse Problemscited by 1
  4. 2024Network scaling and scale-driven loss balancing for intelligent poroelastographycited by 1
  5. 2025A novel Trunk Branch-net PINN for flow and heat transfer prediction in porous mediumcited by 1
  6. 2024Physics-Inspired Deep Learning and Transferable Models for Bridge Scour Predictioncited by 0