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ORCIDSouvik Chakraborty
- > Home > Persons > Souvik Chakraborty
Publications
- 2025
[j23]Navaneeth N.
, Souvik Chakraborty:
Geometry adaptive waveformer for cardio-vascular modeling. Comput. Biol. Medicine 190: 110069 (2025)- [j22]Yash Kumar, Tushar, Souvik Chakraborty:
Energy network for state estimation with random sensors and sparse labels. Comput. Phys. Commun. 311: 109566 (2025) - [j21]Shailesh Garg, Souvik Chakraborty:
Distribution free uncertainty quantification for neuroscience-inspired deep neural operators. J. Comput. Phys. 534: 114012 (2025) - [i59]Rajnish Kumar, Tapas Tripura, Souvik Chakraborty, Sitikantha Roy:
Deep Muscle EMG construction using A Physics-Integrated Deep Learning approach. CoRR abs/2503.05201 (2025) - [i58]Navaneeth N., Souvik Chakraborty:
Geometry adaptive waveformer for cardio-vascular modeling. CoRR abs/2503.17505 (2025) - [i57]Sawan Kumar, Tapas Tripura, Rajdip Nayek, Souvik Chakraborty:
From Local Interactions to Global Operators: Scalable Gaussian Process Operator for Physical Systems. CoRR abs/2506.15906 (2025) - 2024
- [j19]Tapas Tripura, Souvik Chakraborty:
Discovering interpretable Lagrangian of dynamical systems from data. Comput. Phys. Commun. 294: 108960 (2024) - [j16]Shailesh Garg, Souvik Chakraborty:
Neuroscience inspired neural operator for partial differential equations. J. Comput. Phys. 515: 113266 (2024) - [i56]Jyoti Rani, Tapas Tripura, Hariprasad Kodamana, Souvik Chakraborty:
Generative adversarial wavelet neural operator: Application to fault detection and isolation of multivariate time series data. CoRR abs/2401.04004 (2024) - [i54]Sawan Kumar, Rajdip Nayek, Souvik Chakraborty:
Neural Operator induced Gaussian Process framework for probabilistic solution of parametric partial differential equations. CoRR abs/2404.15618 (2024) - [i53]Akshay Thakur, Souvik Chakraborty:
MD-NOMAD: Mixture density nonlinear manifold decoder for emulating stochastic differential equations and uncertainty propagation. CoRR abs/2404.15731 (2024) - [i52]Hartej Soin, Tapas Tripura, Souvik Chakraborty:
Generative flow induced neural architecture search: Towards discovering optimal architecture in wavelet neural operator. CoRR abs/2405.06910 (2024) - [i48]Navaneeth N., Tushar, Souvik Chakraborty:
Harnessing physics-informed operators for high-dimensional reliability analysis problems. CoRR abs/2409.04708 (2024) - [i47]Sawan Kumar, Rajdip Nayek, Souvik Chakraborty:
Towards Gaussian Process for operator learning: an uncertainty aware resolution independent operator learning algorithm for computational mechanics. CoRR abs/2409.10972 (2024) - [i45]Tushar, Sawan Kumar, Souvik Chakraborty:
FUsion-based ConstitutivE model (FuCe): Towards model-data augmentation in constitutive modelling. CoRR abs/2411.03318 (2024) - [i44]Shailesh Garg, Souvik Chakraborty:
Distribution free uncertainty quantification in neuroscience-inspired deep operators. CoRR abs/2412.09369 (2024) - [i43]Isha Jain, Shailesh Garg, Shaurya Shriyam, Souvik Chakraborty:
Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning. CoRR abs/2412.09379 (2024) - 2023
- [j15]Tapas Tripura, Abhilash Awasthi, Sitikantha Roy, Souvik Chakraborty:
A wavelet neural operator based elastography for localization and quantification of tumors. Comput. Methods Programs Biomed. 232: 107436 (2023) - [j14]Shailesh Garg, Souvik Chakraborty:
VB-DeepONet: A Bayesian operator learning framework for uncertainty quantification. Eng. Appl. Artif. Intell. 118: 105685 (2023) - [j12]Tushar, Souvik Chakraborty:
Deep Physics Corrector: A physics enhanced deep learning architecture for solving stochastic differential equations. J. Comput. Phys. 479: 112004 (2023) - [j11]Yogesh Chandrakant Mathpati, Kalpesh Sanjay More, Tapas Tripura, Rajdip Nayek, Souvik Chakraborty:
MAntRA: A framework for model agnostic reliability analysis. Reliab. Eng. Syst. Saf. 235: 109233 (2023) - [i41]Shailesh Garg, Souvik Chakraborty:
Randomized prior wavelet neural operator for uncertainty quantification. CoRR abs/2302.01051 (2023) - [i40]Tapas Tripura, Souvik Chakraborty:
Discovering interpretable Lagrangian of dynamical systems from data. CoRR abs/2302.04400 (2023) - [i39]Navaneeth N., Tapas Tripura, Souvik Chakraborty:
Physics informed WNO. CoRR abs/2302.05925 (2023) - [i38]Kalpesh Sanjay More, Tapas Tripura, Rajdip Nayek, Souvik Chakraborty:
A Bayesian Framework for learning governing Partial Differential Equation from Data. CoRR abs/2306.04894 (2023) - [i37]Aarya Sheetal Desai, Navaneeth N., Sondipon Adhikari, Souvik Chakraborty:
Enhanced multi-fidelity modelling for digital twin and uncertainty quantification. CoRR abs/2306.14430 (2023) - [i36]Yogesh Chandrakant Mathpati, Tapas Tripura, Rajdip Nayek, Souvik Chakraborty:
Discovering stochastic partial differential equations from limited data using variational Bayes inference. CoRR abs/2306.15873 (2023) - [i35]Tushar, Souvik Chakraborty:
DPA-WNO: A gray box model for a class of stochastic mechanics problem. CoRR abs/2309.15128 (2023) - [i34]Navaneeth N., Souvik Chakraborty:
Waveformer for modelling dynamical systems. CoRR abs/2310.04990 (2023) - [i33]Tapas Tripura, Souvik Chakraborty:
A Bayesian framework for discovering interpretable Lagrangian of dynamical systems from data. CoRR abs/2310.06241 (2023) - [i32]Tapas Tripura, Souvik Chakraborty:
A foundational neural operator that continuously learns without forgetting. CoRR abs/2310.18885 (2023) - [i31]Shailesh Garg, Souvik Chakraborty:
Neuroscience inspired scientific machine learning (Part-1): Variable spiking neuron for regression. CoRR abs/2311.09267 (2023) - [i30]Shailesh Garg, Souvik Chakraborty:
Neuroscience inspired scientific machine learning (Part-2): Variable spiking wavelet neural operator. CoRR abs/2311.14710 (2023) - 2022
- [j10]Yash Kumar, Pranav Bahl, Souvik Chakraborty:
State estimation with limited sensors - A deep learning based approach. J. Comput. Phys. 457: 111081 (2022) - [i29]Akshay Thakur, Souvik Chakraborty:
Deep Capsule Encoder-Decoder Network for Surrogate Modeling and Uncertainty Quantification. CoRR abs/2201.07753 (2022) - [i27]Shailesh Garg, Harshit Gupta, Souvik Chakraborty:
Assessment of DeepONet for reliability analysis of stochastic nonlinear dynamical systems. CoRR abs/2201.13145 (2022) - [i26]Navaneeth N., Souvik Chakraborty:
Koopman operator for time-dependent reliability analysis. CoRR abs/2203.02658 (2022) - [i25]Yash Kumar, Souvik Chakraborty:
Energy networks for state estimation with random sensors using sparse labels. CoRR abs/2203.06456 (2022) - [i24]Tapas Tripura, Souvik Chakraborty:
Wavelet neural operator: a neural operator for parametric partial differential equations. CoRR abs/2205.02191 (2022) - [i23]Shailesh Garg, Souvik Chakraborty:
Variational Bayes Deep Operator Network: A data-driven Bayesian solver for parametric differential equations. CoRR abs/2206.05655 (2022) - [i22]Akshay Thakur, Tapas Tripura, Souvik Chakraborty:
Multi-fidelity wavelet neural operator with application to uncertainty quantification. CoRR abs/2208.05606 (2022) - [i21]Tushar, Souvik Chakraborty:
Deep Physics Corrector: A physics enhanced deep learning architecture for solving stochastic differential equations. CoRR abs/2209.09750 (2022) - [i20]Navaneeth N., Souvik Chakraborty:
Stochastic projection based approach for gradient free physics informed learning. CoRR abs/2209.13724 (2022) - [i19]Tapas Tripura, Souvik Chakraborty:
Model-agnostic stochastic model predictive control. CoRR abs/2211.13012 (2022) - [i17]Yogesh Chandrakant Mathpati, Kalpesh Sanjay More, Tapas Tripura, Rajdip Nayek, Souvik Chakraborty:
MAntRA: A framework for model agnostic reliability analysis. CoRR abs/2212.06303 (2022) - [i16]Tapas Tripura, Aarya Sheetal Desai, Sondipon Adhikari, Souvik Chakraborty:
Probabilistic machine learning based predictive and interpretable digital twin for dynamical systems. CoRR abs/2212.09240 (2022) - 2021
- [i15]Yash Kumar, Pranav Bahl, Souvik Chakraborty:
State estimation with limited sensors - A deep learning based approach. CoRR abs/2101.11513 (2021) - [i14]Shailesh Garg, Ankush Gogoi, Souvik Chakraborty, Budhaditya Hazra:
Machine learning based digital twin for stochastic nonlinear multi-degree of freedom dynamical system. CoRR abs/2103.15636 (2021) - [i13]Navaneeth N., Souvik Chakraborty:
Surrogate assisted active subspace and active subspace assisted surrogate - A new paradigm for high dimensional structural reliability analysis. CoRR abs/2105.04979 (2021) - [i12]Yash Kumar, Souvik Chakraborty:
GrADE: A graph based data-driven solver for time-dependent nonlinear partial differential equations. CoRR abs/2108.10639 (2021) - [i11]Tapas Tripura, Mohammad Imran, Budhaditya Hazra, Souvik Chakraborty:
A change of measure enhanced near exact Euler Maruyama scheme for the solution to nonlinear stochastic dynamical systems. CoRR abs/2108.10655 (2021) - [i10]Tapas Tripura, Budhaditya Hazra, Souvik Chakraborty:
Generalized weakly corrected Milstein solutions to stochastic differential equations. CoRR abs/2108.10681 (2021) - [i9]Shailesh Garg, Souvik Chakraborty, Budhaditya Hazra:
Physics-integrated hybrid framework for model form error identification in nonlinear dynamical systems. CoRR abs/2109.00538 (2021) - [i8]Akshay Thakur, Souvik Chakraborty:
A deep learning based surrogate model for stochastic simulators. CoRR abs/2110.13809 (2021) - 2020
- [i6]Souvik Chakraborty, Sondipon Adhikari, Ranjan Ganguli:
The role of surrogate models in the development of digital twins of dynamic systems. CoRR abs/2001.09292 (2020) - [i5]Souvik Chakraborty, Sondipon Adhikari:
Machine learning based digital twin for dynamical systems with multiple time-scales. CoRR abs/2005.05862 (2020) - 2019
- [i3]Rajdip Nayek, Souvik Chakraborty, Sriram Narasimhan:
A Gaussian process latent force model for joint input-state estimation in linear structural systems. CoRR abs/1904.00093 (2019)
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