242 citations · 629 across the 42 of their papers we have counts for
6 papers · 2 filters
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…
Deep Physics Corrector: A physics enhanced deep learning architecture for solving stochastic differential equations
Tushar, Souvik Chakraborty
We propose a novel gray-box modeling algorithm for physical systems governed by stochastic differential equations (SDE). The proposed approach, referred to as the Deep Physics Corr…
Variational Bayes Deep Operator Network: A data-driven Bayesian solver for parametric differential equations
Shailesh Garg, Souvik Chakraborty
Neural network based data-driven operator learning schemes have shown tremendous potential in computational mechanics. DeepONet is one such neural network architecture which has ga…
Koopman operator for time-dependent reliability analysis
Navaneeth N., Souvik Chakraborty
Time-dependent structural reliability analysis of nonlinear dynamical systems is non-trivial; subsequently, scope of most of the structural reliability analysis methods is limited…
Assessment of DeepONet for reliability analysis of stochastic nonlinear dynamical systems
Shailesh Garg, Harshit Gupta, Souvik Chakraborty
Time dependent reliability analysis and uncertainty quantification of structural system subjected to stochastic forcing function is a challenging endeavour as it necessitates consi…
Deep Capsule Encoder-Decoder Network for Surrogate Modeling and Uncertainty Quantification
Akshay Thakur, Souvik Chakraborty
We propose a novel \textit{capsule} based deep encoder-decoder model for surrogate modeling and uncertainty quantification of systems in mechanics from sparse data. The proposed fr…