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
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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 25 Jun. 2026].
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- Título
- Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Autor / colaboradores
- Maziar Raissi; Paris Perdikaris; George Em Karniadakis
- Editorial
- Journal of Computational Physics
- Año de publicación
- 2018
- Idioma
- en
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