428 citations · 1.5k across the 19 of their papers we have counts for
3 papers · 1 filter
A nonlocal physics-informed deep learning framework using the peridynamic differential operator
Ehsan Haghighat, Ali Can Bekar, Erdogan Madenci +1
The Physics-Informed Neural Network (PINN) framework introduced recently incorporates physics into deep learning, and offers a promising avenue for the solution of partial differen…
SciANN: A Keras/Tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks
Ehsan Haghighat, Ruben Juanes
In this paper, we introduce SciANN, a Python package for scientific computing and physics-informed deep learning using artificial neural networks. SciANN uses the widely used deep-…
A deep learning framework for solution and discovery in solid mechanics
Ehsan Haghighat, Maziar Raissi, Adrian Moure +2
We present the application of a class of deep learning, known as Physics Informed Neural Networks (PINN), to learning and discovery in solid mechanics. We explain how to incorporat…