Funder
National Science Foundation
United States Department of Defense | United States Army | U.S. Army Research, Development and Engineering Command | Army Research Office
Publisher
Springer Science and Business Media LLC
Reference168 articles.
1. Brezis, H. & Browder, F. Partial differential equations in the 20th century. Adv. Math. 135, 76–144 (1998).
2. Dissanayake, M. & Phan-Thien, N. Neural-network-based approximations for solving partial differential equations. Commun. Numer. Methods Eng. 10, 195–201 (1994).
3. Rico-Martinez, R. & Kevrekidis, I. G. Continuous time modeling of nonlinear systems: a neural network-based approach. In Proc. IEEE International Conference on Neural Networks 1522–1525 (IEEE, 1993).
4. González-García, R., Rico-Martìnez, R. & Kevrekidis, I. G. Identification of distributed parameter systems: a neural net based approach. Comput. Chem. Eng. 22, S965–S968 (1998).
5. Raissi, M., Perdikaris, P. & Karniadakis, G. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 378, 686–707 (2019).