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PINN

PINNs for solving multiphase poroelasticity relations

Submitted by haghighat on
research
physics-informed neural networks
PINN
poroelasticity

Link to the paper - PINN-Poroelasticity

If you are interested in physics informed neural networks (PINNs) and coupled single and multiphase flow in porous media, please check out our work below: 

- We find it challenging to solve coupled poroelasticity relations using PINNs (data-free).

Exact imposition of boundary conditions in physics-informed neural networks

Submitted by N. Sukumar on
research
deep learning
PINN
meshfree methods

We recently proposed a method that uses distance fields to exactly impose boundary conditions in physics-informed neural networks (PINN).  This contribution is available as an arXiv preprint.

SciANN: Scientific computations and physics-informed deep learning using artificial neural networks

Submitted by haghighat on
research
deep learning
neural networks
physics-informed
elasticity
Plasticity
variational
PINN
regression

Interested in deep learning, scientific computations, solution, and inversion methods for PDE? 

Check out the preprint at: 

https://www.researchgate.net/publication/341478559_SciANN_A_Keras_wrapp…

 

 

Some problems are shared in our GitHub repository on how to use sciann for inversion and forward solution of:

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