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pro vyhledávání: '"Deodhar, Anirudh"'
Deep operator networks (DeepONet) and neural operators have gained significant attention for their ability to map infinite-dimensional function spaces and perform zero-shot super-resolution. However, these models often require large datasets for effe
Externí odkaz:
http://arxiv.org/abs/2409.09207
Deep Operator Networks (DeepONets) and their physics-informed variants have shown significant promise in learning mappings between function spaces of partial differential equations, enhancing the generalization of traditional neural networks. However
Externí odkaz:
http://arxiv.org/abs/2406.14715
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
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
The performance of supervised classification techniques often deteriorates when the data has noisy labels. Even the semi-supervised classification approaches have largely focused only on the problem of handling missing labels. Most of the approaches
Externí odkaz:
http://arxiv.org/abs/2205.02209
Publikováno v:
In Control Engineering Practice January 2023 130
Akademický článek
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Autor:
Deodhar, Anirudh M.
The growth dynamics of isolated gas bubbles (inception → growth → departure) emanating from a capillary-tube orifice submerged in isothermal pools of aqueous solutions of surfactants is computationally investigated. The Navier-Stokes equations ar
Externí odkaz:
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1337100942