5 citations · 22 across the 14 of their papers we have counts for
6 papers · 1 filter
Discovering governing equation in structural dynamics from acceleration-only measurements
Calvin Alvares, Souvik Chakraborty
Over the past few years, equation discovery has gained popularity in different fields of science and engineering. However, existing equation discovery algorithms rely on the availa…
A Bayesian framework for discovering interpretable Lagrangian of dynamical systems from data
Tapas Tripura, Souvik Chakraborty
Learning and predicting the dynamics of physical systems requires a profound understanding of the underlying physical laws. Recent works on learning physical laws involve generaliz…
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…