A-PINN: Auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations
By L. Yuan, Yi Qing Ni, Xiang-Yun Deng, Shuo Hao. Journal of Computational Physics, 2022
371 citations recorded by the source corpus.
Subfields: Numerical Analysis, Statistical and Nonlinear Physics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/bbf4e10e9751780e50edc7760e3bb6d8fdb44696
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Numerical Analysis
In Statistical and Nonlinear Physics
- Gianluigi Rozza
- Fabio Giampaolo
- Salvatore Cuomo
- Francesco Piccialli
- Vincenzo Schiano Di Cola
- Andrea Manzoni
- Maizar Raissi
- G. Em Karniadakis
In Statistics and Probability
- Fabio Giampaolo
- Maizar Raissi
- Salvatore Cuomo
- Francesco Piccialli
- Gianluigi Rozza
- Vincenzo Schiano Di Cola
- Andrea Manzoni
- Alfio Maria Quarteroni
Citations in the corpus
- Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next (2022, background): “Finally, PINNs have also been extended to solve integrodifferential equations (IDEs) [128, 193] or stochastic differential equations (SDEs) [189, 195].”