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pro vyhledávání: '"Jadhav Vishal"'
Autor:
Sarkar, Rajat, Aripirala, Krishna Sai Sudhir, Jadhav, Vishal Sudam, Sakhinana, Sagar Srinivas, Runkana, Venkataramana
Computational Fluid Dynamics (CFD) serves as a powerful tool for simulating fluid flow across diverse industries. High-resolution CFD simulations offer valuable insights into fluid behavior and flow patterns, aiding in optimizing design features or e
Externí odkaz:
http://arxiv.org/abs/2404.04615
Autor:
Sarkar, Rajat Kumar, Majumdar, Ritam, Jadhav, Vishal, Sakhinana, Sagar Srinivas, Runkana, Venkataramana
In Computational Fluid Dynamics (CFD), coarse mesh simulations offer computational efficiency but often lack precision. Applying conventional super-resolution to these simulations poses a significant challenge due to the fundamental contrast between
Externí odkaz:
http://arxiv.org/abs/2311.09740
Autor:
Majumdar, Ritam, Jadhav, Vishal, Deodhar, Anirudh, Karande, Shirish, Vig, Lovekesh, Runkana, Venkataramana
Physics-informed neural networks (PINNs) have been widely used to develop neural surrogates for solutions of Partial Differential Equations. A drawback of PINNs is that they have to be retrained with every change in initial-boundary conditions and PD
Externí odkaz:
http://arxiv.org/abs/2308.09290
Intelligent Electronic Devices (IEDs) are vital components in modern electrical substations, collectively responsible for monitoring electrical parameters and performing protective functions. As a result, ensuring the integrity of IEDs is an essentia
Externí odkaz:
http://arxiv.org/abs/2307.15338
Autor:
Majumdar, Ritam, Jadhav, Vishal, Deodhar, Anirudh, Karande, Shirish, Vig, Lovekesh, Runkana, Venkataramana
Physics-informed Neural Networks (PINNs) have been widely used to obtain accurate neural surrogates for a system of Partial Differential Equations (PDE). One of the major limitations of PINNs is that the neural solutions are challenging to interpret,
Externí odkaz:
http://arxiv.org/abs/2303.07009
Autor:
Majumdar, Ritam, Jadhav, Vishal, Deodhar, Anirudh, Karande, Shirish, Vig, Lovekesh, Runkana, Venkataramana
We demonstrate a Physics-informed Neural Network (PINN) based model for real-time health monitoring of a heat exchanger, that plays a critical role in improving energy efficiency of thermal power plants. A hypernetwork based approach is used to enabl
Externí odkaz:
http://arxiv.org/abs/2212.10032
Autor:
Majumdar, Ritam, Jadhav, Vishal, Deodhar, Anirudh, Karande, Shirish, Vig, Lovekesh, Runkana, Venkataramana
We introduce Physics Informed Symbolic Networks (PISN) which utilize physics-informed loss to obtain a symbolic solution for a system of Partial Differential Equations (PDE). Given a context-free grammar to describe the language of symbolic expressio
Externí odkaz:
http://arxiv.org/abs/2207.06240
Autor:
Gujar, Nishikant N.1, Jadhav, Vishal1, Awati, Jilani S.1, Mudhol, Sajid Ahmad1, H., Md Azmathulla1, Gajakosh, Pradeep1, Talha, Abu1, Alam, Tausif1
Publikováno v:
Al Ameen Journal of Medical Sciences. Oct-Dec2024, Vol. 17 Issue 4, p306-312. 7p.
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Akademický článek
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