33 citations · 70 across the 20 of their papers we have counts for
7 papers · 1 filter
A foundational neural operator that continuously learns without forgetting
Tapas Tripura, Souvik Chakraborty
Machine learning has witnessed substantial growth, leading to the development of advanced artificial intelligence models crafted to address a wide range of real-world challenges sp…
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…
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…
Deep neural operators can predict the real-time response of floating offshore structures under irregular waves
Qianying Cao, Somdatta Goswami, Tapas Tripura +2
The use of neural operators in a digital twin model of an offshore floating structure can provide a paradigm shift in structural response prediction and health monitoring, providin…