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02/09/2023
The FluidFlower International Benchmark Study: Process, Modeling Results, and Comparison to Experimental Data
Successful deployment of geological carbon storage (GCS) requires an ext...
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09/07/2022
Inverse modeling of nonisothermal multiphase poromechanics using physics-informed neural networks
We propose a solution strategy for parameter identification in multiphas...
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03/03/2022
Physics-informed neural network solution of thermo-hydro-mechanical (THM) processes in porous media
Physics-Informed Neural Networks (PINNs) have received increased interes...
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10/06/2021
Physics-informed neural network simulation of multiphase poroelasticity using stress-split sequential training
Physics-informed neural networks (PINNs) have received significant atten...
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05/18/2021
Deep learning for solution and inversion of structural mechanics and vibrations
Deep learning has been the most popular machine learning method in the l...
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05/31/2020
A nonlocal physics-informed deep learning framework using the peridynamic differential operator
The Physics-Informed Neural Network (PINN) framework introduced recently...
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05/11/2020
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 comp...
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02/14/2020
A deep learning framework for solution and discovery in solid mechanics
We present the application of a class of deep learning, known as Physics...
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02/14/2020