Spectral Physics-informed Finite Operator Learning We employed operator learning and combined it with based approaches.
what does that mean:
- Instead of performing AD or trying to build equations by means of coordinates as input we use the Lippmann-Schwinger operator to build physical losses
- Training time is in the same order as data-driven methodologies (even faster :)!)
For more information please check the following: Harandi, Ali, et al. "A Spectral-based Physics-informed Finite Operator Learning for Prediction of Mechanical Behavior of Microstructures." arXiv preprint arXiv:2410.19027 (2024).
Citation: @article{harandi2024spectral, title={A Spectral-based Physics-informed Finite Operator Learning for Prediction of Mechanical Behavior of Microstructures}, author={Harandi, Ali and Danesh, Hooman and Linka, Kevin and Reese, Stefanie and Rezaei, Shahed}, journal={arXiv preprint arXiv:2410.19027}, year={2024} }