5 citations · 16 across the 8 of their papers we have counts for
8 papers
From Local Interactions to Global Operators: Scalable Gaussian Process Operator for Physical Systems
Sawan Kumar, Tapas Tripura, Rajdip Nayek +1
Operator learning offers a powerful paradigm for solving parametric partial differential equations (PDEs), but scaling probabilistic neural operators such as the recently proposed…
Deep Muscle EMG construction using A Physics-Integrated Deep Learning approach
Rajnish Kumar, Tapas Tripura, Souvik Chakraborty +1
Electromyography (EMG)--based computational musculoskeletal modeling is a non-invasive method for studying musculotendon function, human movement, and neuromuscular control, provid…
Generative flow induced neural architecture search: Towards discovering optimal architecture in wavelet neural operator
Hartej Soin, Tapas Tripura, Souvik Chakraborty
We propose a generative flow-induced neural architecture search algorithm. The proposed approach devices simple feed-forward neural networks to learn stochastic policies to generat…
Data-driven discovery of interpretable Lagrangian of stochastically excited dynamical systems
Tapas Tripura, Satyam Panda, Budhaditya Hazra +1
Exploring the intersection of deterministic and stochastic dynamics, this paper delves into Lagrangian discovery for conservative and non-conservative systems under stochastic exci…
Generative adversarial wavelet neural operator: Application to fault detection and isolation of multivariate time series data
Jyoti Rani, Tapas Tripura, Hariprasad Kodamana +1
Fault detection and isolation in complex systems are critical to ensure reliable and efficient operation. However, traditional fault detection methods often struggle with issues su…
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…
Waveformer for modelling dynamical systems
N Navaneeth, Souvik Chakraborty
Neural operators have gained recognition as potent tools for learning solutions of a family of partial differential equations. The state-of-the-art neural operators excel at approx…