2 citations · 3 across the 4 of their papers we have counts for
4 papers
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…
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…
A Bayesian Framework for learning governing Partial Differential Equation from Data
Kalpesh More, Tapas Tripura, Rajdip Nayek +1
The discovery of partial differential equations (PDEs) is a challenging task that involves both theoretical and empirical methods. Machine learning approaches have been developed a…