242 citations · 629 across the 42 of their papers we have counts for
20 papers · 1 filter
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…
Randomized prior wavelet neural operator for uncertainty quantification
Shailesh Garg, Souvik Chakraborty
In this paper, we propose a novel data-driven operator learning framework referred to as the \textit{Randomized Prior Wavelet Neural Operator} (RP-WNO). The proposed RP-WNO is an e…
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…
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…
Physics-integrated hybrid framework for model form error identification in nonlinear dynamical systems
Shailesh Garg, Souvik Chakraborty, Budhaditya Hazra
For real-life nonlinear systems, the exact form of nonlinearity is often not known and the known governing equations are often based on certain assumptions and approximations. Such…
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…
Surrogate assisted active subspace and active subspace assisted surrogate -- A new paradigm for high dimensional structural reliability analysis
Navaneeth N., Souvik Chakraborty
Performing reliability analysis on complex systems is often computationally expensive. In particular, when dealing with systems having high input dimensionality, reliability estima…
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…
Simulation free reliability analysis: A physics-informed deep learning based approach
Souvik Chakraborty
This paper presents a simulation free framework for solving reliability analysis problems. The method proposed is rooted in a recently developed deep learning approach, referred to…
The role of surrogate models in the development of digital twins of dynamic systems
Souvik Chakraborty, Sondipon Adhikari, Ranjan Ganguli
Digital twin technology has significant promise, relevance and potential of widespread applicability in various industrial sectors such as aerospace, infrastructure and automotive.…
Transfer learning enhanced physics informed neural network for phase-field modeling of fracture
Somdatta Goswami, Cosmin Anitescu, Souvik Chakraborty +1
We present a new physics informed neural network (PINN) algorithm for solving brittle fracture problems. While most of the PINN algorithms available in the literature minimize the…