124 citations · 212 across the 18 of their papers we have counts for
9 papers · 1 filter
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…
Gated Linear Model induced U-net for surrogate modeling and uncertainty quantification
Sai Krishna Mendu, Souvik Chakraborty
We propose a novel deep learning based surrogate model for solving high-dimensional uncertainty quantification and uncertainty propagation problems. The proposed deep learning arch…
GrADE: A graph based data-driven solver for time-dependent nonlinear partial differential equations
Yash Kumar, Souvik Chakraborty
The physical world is governed by the laws of physics, often represented in form of nonlinear partial differential equations (PDEs). Unfortunately, solution of PDEs is non-trivial…
Machine learning based digital twin for stochastic nonlinear multi-degree of freedom dynamical system
Shailesh Garg, Ankush Gogoi, Souvik Chakraborty +1
The potential of digital twin technology is immense, specifically in the infrastructure, aerospace, and automotive sector. However, practical implementation of this technology is n…
Machine learning based digital twin for dynamical systems with multiple time-scales
Souvik Chakraborty, Sondipon Adhikari
Digital twin technology has a huge potential for widespread applications in different industrial sectors such as infrastructure, aerospace, and automotive. However, practical adopt…