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Found 7 papers, 3 papers with code
Inverse modeling of nonisothermal multiphase poromechanics using physics-informed neural networks
We propose a solution strategy for parameter identification in multiphase thermo-hydro-mechanical (THM) processes in porous media using physics-informed neural networks (PINNs).
Physics-informed neural network solution of thermo-hydro-mechanical (THM) processes in porous media
Physics-Informed Neural Networks (PINNs) have received increased interest for forward, inverse, and surrogate modeling of problems described by partial differential equations (PDE).
Physics-informed neural network simulation of multiphase poroelasticity using stress-split sequential training
Physics-informed neural networks (PINNs) have received significant attention as a unified framework for forward, inverse, and surrogate modeling of problems governed by partial differential equations (PDEs).
Deep learning for solution and inversion of structural mechanics and vibrations
Deep learning has been the most popular machine learning method in the last few years.
A nonlocal physics-informed deep learning framework using the peridynamic differential operator
The performance of existing PINN approaches, however, may degrade in the presence of sharp gradients, as a result of the inability of the network to capture the solution behavior globally.
SciANN: A Keras/Tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks
In this paper, we introduce SciANN, a Python package for scientific computing and physics-informed deep learning using artificial neural networks.
A deep learning framework for solution and discovery in solid mechanics
We also show the applicability of the framework for transfer learning, and find vastly accelerated convergence during network re-training.
