428 citations · 1.6k across the 21 of their papers we have counts for
6 papers · 1 filter
Physics-Informed Neural Network for Modelling the Thermochemical Curing Process of Composite-Tool Systems During Manufacture
Sina Amini Niaki, Ehsan Haghighat, Trevor Campbell +2
We present a Physics-Informed Neural Network (PINN) to simulate the thermochemical evolution of a composite material on a tool undergoing cure in an autoclave. In particular, we so…
Energy-based error bound of physics-informed neural network solutions in elasticity
Mengwu Guo, Ehsan Haghighat
An energy-based a posteriori error bound is proposed for the physics-informed neural network solutions of elasticity problems. An admissible displacement-stress solution pair is ob…
PINNeik: Eikonal solution using physics-informed neural networks
Umair bin Waheed, Ehsan Haghighat, Tariq Alkhalifah +2
The eikonal equation is utilized across a wide spectrum of science and engineering disciplines. In seismology, it regulates seismic wave traveltimes needed for applications like so…
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…