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pro vyhledávání: '"Ghose, Divij"'
Autor:
Anandh, Thivin, Ghose, Divij, Jain, Himanshu, Sunkad, Pratham, Ganesan, Sashikumaar, John, Volker
This paper proposes and studies two extensions of applying hp-variational physics-informed neural networks, more precisely the FastVPINNs framework, to convection-dominated convection-diffusion-reaction problems. First, a term in the spirit of a SUPG
Externí odkaz:
http://arxiv.org/abs/2411.09329
Autor:
Anandh, Thivin, Ghose, Divij, Tyagi, Ankit, Gupta, Abhineet, Sarkar, Suranjan, Ganesan, Sashikumaar
Physics-informed neural networks (PINNs) are able to solve partial differential equations (PDEs) by incorporating the residuals of the PDEs into their loss functions. Variational Physics-Informed Neural Networks (VPINNs) and hp-VPINNs use the variati
Externí odkaz:
http://arxiv.org/abs/2409.04143
Variational Physics-Informed Neural Networks (VPINNs) utilize a variational loss function to solve partial differential equations, mirroring Finite Element Analysis techniques. Traditional hp-VPINNs, while effective for high-frequency problems, are c
Externí odkaz:
http://arxiv.org/abs/2404.12063