Skip to main content

physics-informed

Defect-based Physics-Informed Machine Learning Framework for Fatigue Prediction

Submitted by enrico.salvati1 on

I would like to draw your attention to our recently proposed predictive method based on a semi-empirical model (LEFM) and Neural Network, exploiting the Physics-informed Machine Learning concept. We show how the accuracy of state-of-the-art fatigue predictive models, based on defects present in materials, can be significantly boosted by accounting for additional morphological features via Physics-Informed Machine Learning.

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

Submitted by haghighat on

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: