33 citations · 70 across the 20 of their papers we have counts for
21 papers
A foundation model for energy and radiation systems built on heterogeneous scientific interfaces
Samrendra Roy, Tapas Tripura, Yoon Pyo Lee +2
Scientific foundation models are commonly evaluated after heterogeneous physical problems have already been translated into a compatible gridded, tokenized or symbolic representati…
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…
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…
Probabilistic machine learning based predictive and interpretable digital twin for dynamical systems
Tapas Tripura, Aarya Sheetal Desai, Sondipon Adhikari +1
A framework for creating and updating digital twins for dynamical systems from a library of physics-based functions is proposed. The sparse Bayesian machine learning is used to upd…
MAntRA: A framework for model agnostic reliability analysis
Yogesh Chandrakant Mathpati, Kalpesh Sanjay More, Tapas Tripura +2
We propose a novel model agnostic data-driven reliability analysis framework for time-dependent reliability analysis. The proposed approach -- referred to as MAntRA -- combines int…
Model-agnostic stochastic model predictive control
Tapas Tripura, Souvik Chakraborty
We propose a model-agnostic stochastic predictive control (MASMPC) algorithm for dynamical systems. The proposed approach first discovers \textit{interpretable} governing different…
Learning governing physics from output only measurements
Tapas Tripura, Souvik Chakraborty
Extracting governing physics from data is a key challenge in many areas of science and technology. The existing techniques for equations discovery are dependent on both input and s…
Multi-fidelity wavelet neural operator with application to uncertainty quantification
Akshay Thakur, Tapas Tripura, Souvik Chakraborty
Operator learning frameworks, because of their ability to learn nonlinear maps between two infinite dimensional functional spaces and utilization of neural networks in doing so, ha…
Wavelet neural operator: a neural operator for parametric partial differential equations
Tapas Tripura, Souvik Chakraborty
With massive advancements in sensor technologies and Internet-of-things, we now have access to terabytes of historical data; however, there is a lack of clarity in how to best expl…
Generalized weakly corrected Milstein solutions to stochastic differential equations
Tapas Tripura, Budhaditya Hazra, Souvik Chakraborty
In this work, weakly corrected explicit, semi-implicit and implicit Milstein approximations are presented for the solution of nonlinear stochastic differential equations. The solut…
A change of measure enhanced near exact Euler Maruyama scheme for the solution to nonlinear stochastic dynamical systems
Tapas Tripura, Mohammad Imran, Budhaditya Hazra +1
The present study utilizes the Girsanov transformation based framework for solving a nonlinear stochastic dynamical system in an efficient way in comparison to other available appr…