33 citations · 70 across the 20 of their papers we have counts for
6 papers · 1 filter
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…