124 citations · 190 across the 14 of their papers we have counts for
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Towards Gaussian Process for operator learning: an uncertainty aware resolution independent operator learning algorithm for computational mechanics
Sawan Kumar, Rajdip Nayek, Souvik Chakraborty
The growing demand for accurate, efficient, and scalable solutions in computational mechanics highlights the need for advanced operator learning algorithms that can efficiently han…
Alpha-VI DeepONet: A prior-robust variational Bayesian approach for enhancing DeepONets with uncertainty quantification
Soban Nasir Lone, Subhayan De, Rajdip Nayek
We introduce a novel deep operator network (DeepONet) framework that incorporates generalised variational inference (GVI) using Rényi's -divergence to learn complex operators wh…
Neural Operator induced Gaussian Process framework for probabilistic solution of parametric partial differential equations
Sawan Kumar, Rajdip Nayek, Souvik Chakraborty
The study of neural operators has paved the way for the development of efficient approaches for solving partial differential equations (PDEs) compared with traditional methods. How…
Physics Informed Neural Networks for Free Shear Flows
Siddharth Raghu, Rajdip Nayek, Vamsi Chalamalla
The transformative impact of machine learning, particularly Deep Learning (DL), on scientific and engineering domains is evident. In the context of computational fluid dynamics (CF…