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06/07/2023
A novel deeponet model for learning moving-solution operators with applications to earthquake hypocenter localization
Seismicity induced by human activities poses a significant threat to pub...
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03/13/2023
An efficient phase-field model of shear fractures using deviatoric stress split
We propose a phase-field model of shear fractures using the deviatoric s...
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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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03/18/2022
Constitutive model characterization and discovery using physics-informed deep learning
Classically, the mechanical response of materials is described through c...
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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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08/16/2021
A Physics Informed Neural Network Approach to Solution and Identification of Biharmonic Equations of Elasticity
We explore an application of the Physics Informed Neural Networks (PINNs...
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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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11/27/2020
Physics-Informed Neural Network for Modelling the Thermochemical Curing Process of Composite-Tool Systems During Manufacture
We present a Physics-Informed Neural Network (PINN) to simulate the ther...
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10/18/2020
An energy-based error bound of physics-informed neural network solutions in elasticity
An energy-based a posteriori error bound is proposed for the physics-inf...
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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