Zobrazeno 1 - 10
of 136
pro vyhledávání: '"Ganesan, Sashikumaar"'
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
Autor:
Yadav, Sangeeta, Ganesan, Sashikumaar
An artificial intelligence-augmented Streamline Upwind/Petrov-Galerkin finite element scheme (AiStab-FEM) is proposed for solving singularly perturbed partial differential equations. In particular, an artificial neural network framework is proposed t
Externí odkaz:
http://arxiv.org/abs/2211.13418
An operator-splitting finite element scheme for the time-dependent, high-dimensional radiative transfer equation is presented in this paper. The streamline upwind Petrov-Galerkin finite element method and discontinuous Galerkin finite element method
Externí odkaz:
http://arxiv.org/abs/2112.07949
Autor:
Yadav, Sangeeta, Ganesan, Sashikumaar
Publikováno v:
In Journal of Computational Physics 15 February 2024 499
Autor:
Garg, Deepika, Ganesan, Sashikumaar
A priori analysis for a generalized local projection stabilized conforming finite element approximation of Darcy flow and Stokes problems is presented in this paper. A first-order conforming P1 finite element space is used to approximate both the vel
Externí odkaz:
http://arxiv.org/abs/2009.00571
Autor:
Garg, Deepika, Ganesan, Sashikumaar
A priori analysis for a generalized local projection stabilized finite element approximations for the solution of an advection-reaction equation is presented in this article. The stability and a priori error estimates are established for both the con
Externí odkaz:
http://arxiv.org/abs/2009.00532
Autor:
Ganesan, Sashikumaar, Shah, Manan
Hybrid CPU-GPU algorithms for Algebraic Multigrid methods (AMG) to efficiently utilize both CPU and GPU resources are presented. In particular, hybrid AMG framework focusing on minimal utilization of GPU memory with performance on par with GPU-only i
Externí odkaz:
http://arxiv.org/abs/2007.00056