242 citations · 629 across the 42 of their papers we have counts for
5 papers · 2 filters
Discovering stochastic partial differential equations from limited data using variational Bayes inference
Yogesh Chandrakant Mathpati, Tapas Tripura, Rajdip Nayek +1
We propose a novel framework for discovering Stochastic Partial Differential Equations (SPDEs) from data. The proposed approach combines the concepts of stochastic calculus, variat…
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…
Physics informed WNO
Navaneeth N, Tapas Tripura, Souvik Chakraborty
Deep neural operators are recognized as an effective tool for learning solution operators of complex partial differential equations (PDEs). As compared to laborious analytical and…
Discovering interpretable Lagrangian of dynamical systems from data
Tapas Tripura, Souvik Chakraborty
A complete understanding of physical systems requires models that are accurate and obeys natural conservation laws. Recent trends in representation learning involve learning Lagran…
Randomized prior wavelet neural operator for uncertainty quantification
Shailesh Garg, Souvik Chakraborty
In this paper, we propose a novel data-driven operator learning framework referred to as the \textit{Randomized Prior Wavelet Neural Operator} (RP-WNO). The proposed RP-WNO is an e…