Modelling uncertainty in incompressible flow simulation using Galerkin based generalized ANOVA. - Souvik Chakraborty; Rajib Chowdhury (Comput. Phys. Commun., 2016)

Sequential experimental design based generalised ANOVA. - Souvik Chakraborty; Rajib Chowdhury (J. Comput. Phys., 2016)

Moment Independent Sensitivity Analysis: H-PCFE-Based Approach. - Souvik Chakraborty; Rajib Chowdhury (J. Comput. Civ. Eng., 2017)

An efficient algorithm for building locally refined hp - adaptive H-PCFE: Application to uncertainty quantification. - Souvik Chakraborty; Rajib Chowdhury (J. Comput. Phys., 2017)

A hybrid approach for global sensitivity analysis. - Souvik Chakraborty; Rajib Chowdhury (Reliab. Eng. Syst. Saf., 2017)

Efficient data-driven reduced-order models for high-dimensional multiscale dynamical systems. - Souvik Chakraborty; Nicholas Zabaras (Comput. Phys. Commun., 2018)

Hybrid Reliability Analysis Framework for Reliability Analysis of Tunnels. - Souvik Chakraborty; Dipaloke Majumder (J. Comput. Civ. Eng., 2018)

Dynamical accelerated performance measure approach for efficient reliability-based design optimization with highly nonlinear probabilistic constraints. - Behrooz Keshtegar; Souvik Chakraborty (Reliab. Eng. Syst. Saf., 2018)

A Gaussian process latent force model for joint input-state estimation in linear structural systems. - Rajdip Nayek; Souvik Chakraborty; Sriram Narasimhan (CoRR, 2019)

Threshold shift method for reliability-based design optimization. - Somdatta Goswami; Souvik Chakraborty; Rajib Chowdhury; Timon Rabczuk (CoRR, 2019)

Transfer learning enhanced physics informed neural network for phase-field modeling of fracture. - Somdatta Goswami; Cosmin Anitescu; Souvik Chakraborty; Timon Rabczuk (CoRR, 2019)

The role of surrogate models in the development of digital twins of dynamic systems. - Souvik Chakraborty; Sondipon Adhikari; Ranjan Ganguli (CoRR, 2020)

Machine learning based digital twin for dynamical systems with multiple time-scales. - Souvik Chakraborty; Sondipon Adhikari (CoRR, 2020)

Machine learning based digital twin for stochastic nonlinear multi-degree of freedom dynamical system. - Shailesh Garg 001; Ankush Gogoi; Souvik Chakraborty; Budhaditya Hazra (CoRR, 2021)

Surrogate assisted active subspace and active subspace assisted surrogate - A new paradigm for high dimensional structural reliability analysis. - Navaneeth N.; Souvik Chakraborty (CoRR, 2021)

GrADE: A graph based data-driven solver for time-dependent nonlinear partial differential equations. - Yash Kumar; Souvik Chakraborty (CoRR, 2021)

A change of measure enhanced near exact Euler Maruyama scheme for the solution to nonlinear stochastic dynamical systems. - Tapas Tripura; Mohammad Imran; Budhaditya Hazra; Souvik Chakraborty (CoRR, 2021)

Generalized weakly corrected Milstein solutions to stochastic differential equations. - Tapas Tripura; Budhaditya Hazra; Souvik Chakraborty (CoRR, 2021)

Physics-integrated hybrid framework for model form error identification in nonlinear dynamical systems. - Shailesh Garg 001; Souvik Chakraborty; Budhaditya Hazra (CoRR, 2021)

A deep learning based surrogate model for stochastic simulators. - Akshay Thakur; Souvik Chakraborty (CoRR, 2021)

Gated Linear Model induced U-net for surrogate modeling and uncertainty quantification. - Sai Krishna Mendu; Souvik Chakraborty (CoRR, 2021)

Transfer learning based multi-fidelity physics informed deep neural network. - Souvik Chakraborty (J. Comput. Phys., 2021)

Deep Capsule Encoder-Decoder Network for Surrogate Modeling and Uncertainty Quantification. - Akshay Thakur; Souvik Chakraborty (CoRR, 2022)

Assessment of DeepONet for reliability analysis of stochastic nonlinear dynamical systems. - Shailesh Garg 001; Harshit Gupta; Souvik Chakraborty (CoRR, 2022)

Koopman operator for time-dependent reliability analysis. - Navaneeth N.; Souvik Chakraborty (CoRR, 2022)

Energy networks for state estimation with random sensors using sparse labels. - Yash Kumar; Souvik Chakraborty (CoRR, 2022)

Wavelet neural operator: a neural operator for parametric partial differential equations. - Tapas Tripura; Souvik Chakraborty (CoRR, 2022)

Variational Bayes Deep Operator Network: A data-driven Bayesian solver for parametric differential equations. - Shailesh Garg 001; Souvik Chakraborty (CoRR, 2022)

Multi-fidelity wavelet neural operator with application to uncertainty quantification. - Akshay Thakur; Tapas Tripura; Souvik Chakraborty (CoRR, 2022)

Stochastic projection based approach for gradient free physics informed learning. - Navaneeth N.; Souvik Chakraborty (CoRR, 2022)

Model-agnostic stochastic model predictive control. - Tapas Tripura; Souvik Chakraborty (CoRR, 2022)

Physics-Informed Multi-Stage Deep Learning Framework Development for Digital Twin-Centred State-Based Reactor Power Prediction. - James Daniell; Kazuma Kobayashi; Susmita Naskar; Dinesh Kumar; Souvik Chakraborty; Ayodeji Alajo; Ethan Taber; Joseph Graham; Syed B. Alam (CoRR, 2022)

Probabilistic machine learning based predictive and interpretable digital twin for dynamical systems. - Tapas Tripura; Aarya Sheetal Desai; Sondipon Adhikari; Souvik Chakraborty (CoRR, 2022)

State estimation with limited sensors - A deep learning based approach. - Yash Kumar; Pranav Bahl; Souvik Chakraborty (J. Comput. Phys., 2022)

Explainable, Interpretable & Trustworthy AI for Intelligent Digital Twin: Case Study on Remaining Useful Life. - Kazuma Kobayashi; Bader Almutairi; Md. Nazmus Sakib; Souvik Chakraborty; Syed B. Alam (CoRR, 2023)

Randomized prior wavelet neural operator for uncertainty quantification. - Shailesh Garg 001; Souvik Chakraborty (CoRR, 2023)

Physics informed WNO. - Navaneeth N.; Tapas Tripura; Souvik Chakraborty (CoRR, 2023)

A Bayesian Framework for learning governing Partial Differential Equation from Data. - Kalpesh Sanjay More; Tapas Tripura; Rajdip Nayek; Souvik Chakraborty (CoRR, 2023)

Enhanced multi-fidelity modelling for digital twin and uncertainty quantification. - Aarya Sheetal Desai; Navaneeth N.; Sondipon Adhikari; Souvik Chakraborty (CoRR, 2023)

Discovering stochastic partial differential equations from limited data using variational Bayes inference. - Yogesh Chandrakant Mathpati; Tapas Tripura; Rajdip Nayek; Souvik Chakraborty (CoRR, 2023)

DPA-WNO: A gray box model for a class of stochastic mechanics problem. - Tushar; Souvik Chakraborty (CoRR, 2023)

Waveformer for modelling dynamical systems. - Navaneeth N.; Souvik Chakraborty (CoRR, 2023)

A Bayesian framework for discovering interpretable Lagrangian of dynamical systems from data. - Tapas Tripura; Souvik Chakraborty (CoRR, 2023)

A foundational neural operator that continuously learns without forgetting. - Tapas Tripura; Souvik Chakraborty (CoRR, 2023)

Neuroscience inspired scientific machine learning (Part-1): Variable spiking neuron for regression. - Shailesh Garg 001; Souvik Chakraborty (CoRR, 2023)

Neuroscience inspired scientific machine learning (Part-2): Variable spiking wavelet neural operator. - Shailesh Garg 001; Souvik Chakraborty (CoRR, 2023)

VB-DeepONet: A Bayesian operator learning framework for uncertainty quantification. - Shailesh Garg 001; Souvik Chakraborty (Eng. Appl. Artif. Intell., 2023)

A wavelet neural operator based elastography for localization and quantification of tumors. - Tapas Tripura; Abhilash Awasthi; Sitikantha Roy; Souvik Chakraborty (Comput. Methods Programs Biomed., 2023)

Deep Physics Corrector: A physics enhanced deep learning architecture for solving stochastic differential equations. - Tushar; Souvik Chakraborty (J. Comput. Phys., 2023)

MAntRA: A framework for model agnostic reliability analysis. - Yogesh Chandrakant Mathpati; Kalpesh Sanjay More; Tapas Tripura; Rajdip Nayek; Souvik Chakraborty (Reliab. Eng. Syst. Saf., 2023)

Efficient hybrid topology optimization using GPU and homogenization-based multigrid approach. - Arya Prakash Padhi; Souvik Chakraborty; Anupam Chakrabarti; Rajib Chowdhury (Eng. Comput., 2023)

Generative adversarial wavelet neural operator: Application to fault detection and isolation of multivariate time series data. - Jyoti Rani; Tapas Tripura; Hariprasad Kodamana; Souvik Chakraborty (CoRR, 2024)

PhyPlan: Compositional and Adaptive Physical Task Reasoning with Physics-Informed Skill Networks for Robot Manipulators. - Harshil Vagadia; Mudit Chopra; Abhinav Barnawal; Tamajit Banerjee; Shreshth Tuli; Souvik Chakraborty; Rohan Paul (CoRR, 2024)

Neural Operator induced Gaussian Process framework for probabilistic solution of parametric partial differential equations. - Sawan Kumar; Rajdip Nayek; Souvik Chakraborty (CoRR, 2024)

MD-NOMAD: Mixture density nonlinear manifold decoder for emulating stochastic differential equations and uncertainty propagation. - Akshay Thakur; Souvik Chakraborty (CoRR, 2024)

PhyPlan: Generalizable and Rapid Physical Task Planning with Physics Informed Skill Networks for Robot Manipulators. - Mudit Chopra; Abhinav Barnawal; Harshil Vagadia; Tamajit Banerjee; Shreshth Tuli; Souvik Chakraborty; Rohan Paul (CoRR, 2024)

Discovering governing equation in structural dynamics from acceleration-only measurements. - Calvin Alvares; Souvik Chakraborty (CoRR, 2024)

Spatio-spectral graph neural operator for solving computational mechanics problems on irregular domain and unstructured grid. - Subhankar Sarkar; Souvik Chakraborty (CoRR, 2024)

Harnessing physics-informed operators for high-dimensional reliability analysis problems. - Navaneeth N.; Tushar; Souvik Chakraborty (CoRR, 2024)

Towards Gaussian Process for operator learning: an uncertainty aware resolution independent operator learning algorithm for computational mechanics. - Sawan Kumar; Rajdip Nayek; Souvik Chakraborty (CoRR, 2024)

G2TR: Generalized Grounded Temporal Reasoning for Robot Instruction Following by Combining Large Pre-trained Models. - Riya Arora; Niveditha Narendranath; Aman Tambi; Sandeep S. Zachariah; Souvik Chakraborty; Rohan Paul (CoRR, 2024)

FUsion-based ConstitutivE model (FuCe): Towards model-data augmentation in constitutive modelling. - Tushar; Sawan Kumar; Souvik Chakraborty (CoRR, 2024)

Distribution free uncertainty quantification in neuroscience-inspired deep operators. - Shailesh Garg 001; Souvik Chakraborty (CoRR, 2024)

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning. - Isha Jain; Shailesh Garg 001; Shaurya Shriyam; Souvik Chakraborty (CoRR, 2024)

Deep learning accelerated efficient framework for topology optimization. - Arya Prakash Padhi; Souvik Chakraborty; Anupam Chakrabarti; Rajib Chowdhury (Eng. Appl. Artif. Intell., 2024)

Continuous control of structural vibrations using hybrid deep reinforcement learning policy. - Jagajyoti Panda; Mudit Chopra; Vasant Matsagar; Souvik Chakraborty (Expert Syst. Appl., 2024)

Neuroscience inspired neural operator for partial differential equations. - Shailesh Garg 001; Souvik Chakraborty (J. Comput. Phys., 2024)

Discovering interpretable Lagrangian of dynamical systems from data. - Tapas Tripura; Souvik Chakraborty (Comput. Phys. Commun., 2024)

Geometry adaptive waveformer for cardio-vascular modeling. - Navaneeth N.; Souvik Chakraborty (Comput. Biol. Medicine, 2025)

Energy network for state estimation with random sensors and sparse labels. - Yash Kumar; Tushar; Souvik Chakraborty (Comput. Phys. Commun., 2025)

Generative flow induced neural architecture search: Towards discovering optimal architecture in wavelet neural operator. - Hartej Soin; Tapas Tripura; Souvik Chakraborty (Comput. Phys. Commun., 2025)

Deep muscle electromyogram construction using a physics-integrated deep learning approach. - Rajnish Kumar; Tapas Tripura; Souvik Chakraborty; Sitikantha Roy (Eng. Appl. Artif. Intell., 2025)

A Bayesian approach for discovering time- delayed differential equation from data. - Debangshu Chowdhury; Souvik Chakraborty (J. Comput. Phys., 2025)

Distribution free uncertainty quantification for neuroscience-inspired deep neural operators. - Shailesh Garg 001; Souvik Chakraborty (J. Comput. Phys., 2025)

Deep Muscle EMG construction using A Physics-Integrated Deep Learning approach. - Rajnish Kumar; Tapas Tripura; Souvik Chakraborty; Sitikantha Roy (CoRR, 2025)

From Local Interactions to Global Operators: Scalable Gaussian Process Operator for Physical Systems. - Sawan Kumar; Tapas Tripura; Rajdip Nayek; Souvik Chakraborty (CoRR, 2025)

Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators. - Kazuma Kobayashi; Shailesh Garg 001; Farid Ahmed; Souvik Chakraborty; Syed Bahauddin Alam (CoRR, 2025)

Physics- and geometry-aware spatio-spectral graph neural operator for time-independent and time-dependent PDEs. - Subhankar Sarkar; Souvik Chakraborty (CoRR, 2025)