Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Maziar Raissi; Paris Perdikaris; George Em Karniadakis · Journal of Computational Physics · 2018
Resource access
Open the content from the main option or choose another available source.
Resource page
How to cite
Elegí el formato que necesitás y copiá la referencia al portapapeles.
APA 7
Raissi, M, Perdikaris, P, & Karniadakis, G. E. (2018). Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. https://doi.org/10.1016/j.jcp.2018.10.045
MLA
Raissi, Maziar, et al. "Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations." 2018. https://doi.org/10.1016/j.jcp.2018.10.045.
Chicago
Raissi, Maziar, Paris Perdikaris, and George Em Karniadakis. 2018. "Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations.". https://doi.org/10.1016/j.jcp.2018.10.045.
Harvard
Raissi, M, Perdikaris, P. and Karniadakis, G. E. 2018, Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations, Journal of Computational Physics, available at: https://doi.org/10.1016/j.jcp.2018.10.045 [Accessed 6 Aug. 2026].
Resource details
Bibliographic information to help confirm that this is the correct material.
- Title
- Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Author / contributors
- Maziar Raissi; Paris Perdikaris; George Em Karniadakis
- Publisher
- Journal of Computational Physics
- Publication year
- 2018
- Language
- English
Subjects
Explore related resources through these subjects.