Souvik Chakraborty
Orcid: 0000-0003-2383-2603
According to our database1,
Souvik Chakraborty
authored at least 69 papers
between 2016 and 2024.
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Bibliography
2024
Comput. Phys. Commun., January, 2024
J. Comput. Phys., 2024
Continuous control of structural vibrations using hybrid deep reinforcement learning policy.
Expert Syst. Appl., 2024
Eng. Appl. Artif. Intell., 2024
Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning.
CoRR, 2024
Distribution free uncertainty quantification in neuroscience-inspired deep operators.
CoRR, 2024
FUsion-based ConstitutivE model (FuCe): Towards model-data augmentation in constitutive modelling.
CoRR, 2024
G<sup>2</sup>TR: Generalized Grounded Temporal Reasoning for Robot Instruction Following by Combining Large Pre-trained Models.
CoRR, 2024
Towards Gaussian Process for operator learning: an uncertainty aware resolution independent operator learning algorithm for computational mechanics.
CoRR, 2024
Harnessing physics-informed operators for high-dimensional reliability analysis problems.
CoRR, 2024
Spatio-spectral graph neural operator for solving computational mechanics problems on irregular domain and unstructured grid.
CoRR, 2024
Discovering governing equation in structural dynamics from acceleration-only measurements.
CoRR, 2024
PhyPlan: Generalizable and Rapid Physical Task Planning with Physics Informed Skill Networks for Robot Manipulators.
CoRR, 2024
Generative flow induced neural architecture search: Towards discovering optimal architecture in wavelet neural operator.
CoRR, 2024
MD-NOMAD: Mixture density nonlinear manifold decoder for emulating stochastic differential equations and uncertainty propagation.
CoRR, 2024
Neural Operator induced Gaussian Process framework for probabilistic solution of parametric partial differential equations.
CoRR, 2024
PhyPlan: Compositional and Adaptive Physical Task Reasoning with Physics-Informed Skill Networks for Robot Manipulators.
CoRR, 2024
Generative adversarial wavelet neural operator: Application to fault detection and isolation of multivariate time series data.
CoRR, 2024
2023
Efficient hybrid topology optimization using GPU and homogenization-based multigrid approach.
Eng. Comput., October, 2023
Reliab. Eng. Syst. Saf., July, 2023
Deep Physics Corrector: A physics enhanced deep learning architecture for solving stochastic differential equations.
J. Comput. Phys., April, 2023
A wavelet neural operator based elastography for localization and quantification of tumors.
Comput. Methods Programs Biomed., April, 2023
Eng. Appl. Artif. Intell., 2023
Neuroscience inspired scientific machine learning (Part-2): Variable spiking wavelet neural operator.
CoRR, 2023
Neuroscience inspired scientific machine learning (Part-1): Variable spiking neuron for regression.
CoRR, 2023
CoRR, 2023
A Bayesian framework for discovering interpretable Lagrangian of dynamical systems from data.
CoRR, 2023
Discovering stochastic partial differential equations from limited data using variational Bayes inference.
CoRR, 2023
CoRR, 2023
CoRR, 2023
Explainable, Interpretable & Trustworthy AI for Intelligent Digital Twin: Case Study on Remaining Useful Life.
CoRR, 2023
2022
J. Comput. Phys., 2022
Probabilistic machine learning based predictive and interpretable digital twin for dynamical systems.
CoRR, 2022
Physics-Informed Multi-Stage Deep Learning Framework Development for Digital Twin-Centred State-Based Reactor Power Prediction.
CoRR, 2022
CoRR, 2022
Multi-fidelity wavelet neural operator with application to uncertainty quantification.
CoRR, 2022
Variational Bayes Deep Operator Network: A data-driven Bayesian solver for parametric differential equations.
CoRR, 2022
Wavelet neural operator: a neural operator for parametric partial differential equations.
CoRR, 2022
CoRR, 2022
Assessment of DeepONet for reliability analysis of stochastic nonlinear dynamical systems.
CoRR, 2022
Deep Capsule Encoder-Decoder Network for Surrogate Modeling and Uncertainty Quantification.
CoRR, 2022
2021
J. Comput. Phys., 2021
Gated Linear Model induced U-net for surrogate modeling and uncertainty quantification.
CoRR, 2021
Physics-integrated hybrid framework for model form error identification in nonlinear dynamical systems.
CoRR, 2021
Generalized weakly corrected Milstein solutions to stochastic differential equations.
CoRR, 2021
A change of measure enhanced near exact Euler Maruyama scheme for the solution to nonlinear stochastic dynamical systems.
CoRR, 2021
GrADE: A graph based data-driven solver for time-dependent nonlinear partial differential equations.
CoRR, 2021
Surrogate assisted active subspace and active subspace assisted surrogate - A new paradigm for high dimensional structural reliability analysis.
CoRR, 2021
Machine learning based digital twin for stochastic nonlinear multi-degree of freedom dynamical system.
CoRR, 2021
2020
CoRR, 2020
CoRR, 2020
2019
Transfer learning enhanced physics informed neural network for phase-field modeling of fracture.
CoRR, 2019
A Gaussian process latent force model for joint input-state estimation in linear structural systems.
CoRR, 2019
2018
Dynamical accelerated performance measure approach for efficient reliability-based design optimization with highly nonlinear probabilistic constraints.
Reliab. Eng. Syst. Saf., 2018
J. Comput. Civ. Eng., 2018
Efficient data-driven reduced-order models for high-dimensional multiscale dynamical systems.
Comput. Phys. Commun., 2018
2017
An efficient algorithm for building locally refined hp - adaptive H-PCFE: Application to uncertainty quantification.
J. Comput. Phys., 2017
J. Comput. Civ. Eng., 2017
2016
Modelling uncertainty in incompressible flow simulation using Galerkin based generalized ANOVA.
Comput. Phys. Commun., 2016