# Publications

## Pre-print

Soin, H., Tripura, T., & Chakraborty, S. (2024). Generative flow induced neural architecture search: Towards discovering optimal architecture in wavelet neural operator. arXiv preprint arXiv:2405.06910.

Thakur, A., & Chakraborty, S. (2024). MD-NOMAD: Mixture density nonlinear manifold decoder for emulating stochastic differential equations and uncertainty propagation. arXiv preprint arXiv:2404.15731.

Vagadia, H., Chopra, M., Barnawal, A., Banerjee, T., Tuli, S., Chakraborty, S., & Paul, R. (2024). PhyPlan: Compositional and Adaptive Physical Task Reasoning with Physics-Informed Skill Networks for Robot Manipulators. arXiv preprint arXiv:2402.15767.

Rani, J., Tripura, T., Kodamana, H., & Chakraborty, S. (2024). Generative adversarial wavelet neural operator: Application to fault detection and isolation of multivariate time series data. arXiv preprint arXiv:2401.04004.

Garg, S., & Chakraborty, S. (2023). Neuroscience inspired scientific machine learning (Part-2): Variable spiking wavelet neural operator. arXiv preprint arXiv:2311.14710.

Garg, S., & Chakraborty, S. (2023). Neuroscience inspired scientific machine learning (Part-1): Variable spiking neuron for regression. arXiv preprint arXiv:2311.09267.

Tripura, T., & Chakraborty, S. (2023). A foundational neural operator that continuously learns without forgetting. arXiv preprint arXiv:2310.18885.

Tushar & Chakraborty, S. (2023). DPA-WNO: A gray box model for a class of stochastic mechanics problem. arXiv preprint arXiv:2309.15128.

Garg, S. and Chakraborty, S. Randomized prior wavelet neural operator for uncertainty quantification, arXiv preprint:2302.01051

Kobayashi, K., Almutairi, B., Sakib, M.N., Chakraborty, S. and Alam, S.B. Explainable, Interpretable & Trustworthy AI for Intelligent Digital Twin: Case Study on Remaining Useful Life. arXiv preprint:2301.06676.

Kobayashi, K., Kumar, D., Naskar, S., Chakraborty, S., Paaren, K., Graham, J. and Alam, S. Uncertainty Quantification and Sensitivity analysis for Digital Twin Enabling Technology for Accident Tolerant Fuel: Application for BISON Fuel Performance Code. arXiv preprint:2211.13687.

Daniell, J., Kobayashi, K., Kumar, D., Chakraborty, S., Alajo, A., Taber, E., Graham, J. and Alam, S. Physics-Informed Multi-Stage Deep Learning Framework Development for Digital Twin-Centred State-Based Reactor Power Prediction. arXiv preprint arXiv:2211.13157..

Kumar, Y. and Chakraborty, S. Energy networks for state estimation with random sensors using sparse labels. arXiv preprint arXiv:2203.06456.

Kumar, Y. and Chakraborty, S. GrADE: A graph based data-driven solver for time-dependent nonlinear partial differential equations. arXiv preprint: 2108.10639

Chakraborty, S. Simulation free reliability analysis: A physics-informed deep learning based approach. arXiv preprint:2005.01302.

## 2024

Garg, S., & Chakraborty, S. (2024). Neuroscience inspired neural operator for partial differential equations. Journal of Computational Physics, 515, 113266.

Kumar, S., Nayek, R., & Chakraborty, S. (2024). Neural Operator induced Gaussian Process framework for probabilistic solution of parametric partial differential equations. Computer Methods in Applied Mechanics and Engineering, 431,117265.

Tripura, T., & Chakraborty, S. (2024). A Bayesian framework for discovering interpretable Lagrangian of dynamical systems from data. Mechanical Systems and Signal Processing, 221, 111737.

Tripura, T., Thakur, A., & Chakraborty, S. (2024). Multi-fidelity wavelet neural operator surrogate model for time-independent and time-dependent reliability analysis. Probabilistic Engineering Mechanics, 103672 (in press).

Padhi, A. P., Chakraborty, S., Chakrabarti, A., & Chowdhury, R. (2024). Deep learning accelerated efficient framework for topology optimization. Engineering Applications of Artificial Intelligence, 133, 108559.

Tripura, T., Panda, S., Hazra, B., & Chakraborty, S. (2024). Data-driven discovery of interpretable Lagrangian of stochastically excited dynamical systems. Computer Methods in Applied Mechanics and Engineering, 427, 117032.

Herath, S., & Chakraborty, S. Nonlinear response modelling of material systems using constrained Gaussian processes. International Journal for Numerical Methods in Engineering, e7486.

Panda, J., Chopra, M., Matsagar, V., & Chakraborty, S. (2024). Continuous control of structural vibrations using hybrid deep reinforcement learning policy. Expert Systems with Applications, 252 (Part-A), 124075.

Navaneeth, N., & Chakraborty, S. (2024). Waveformer for modeling dynamical systems. Mechanical Systems and Signal Processing, 211, 111253.

Mahata, A., Mukhopadhyay, T., Chakraborty, S., & Asle Zaeem, M. (2024). Atomistic simulation assisted error-inclusive Bayesian machine learning for probabilistically unraveling the mechanical properties of solidified metals. npj Computational Materials, 10(1), 22.

Cao, Q., Goswami, S., Tripura, T., Chakraborty, S., & Karniadakis, G. E. (2024). Deep neural operators can predict the real-time response of floating offshore structures under irregular waves. Computers & Structures, 291, 107228.

Navaneeth, N., Tripura, T., & Chakraborty, S. (2024). Physics informed WNO. Computer Methods in Applied Mechanics and Engineering, 418, 116546.

Tripura, T., & Chakraborty, S. (2024). Discovering interpretable Lagrangian of dynamical systems from data. Computer Physics Communications, 294, 108960.

Mathpati, Y. C., Tripura, T., Nayek, R., & Chakraborty, S. (2024). Discovering stochastic partial differential equations from limited data using variational Bayes inference. Computer Methods in Applied Mechanics and Engineering, 418, 116512.

Panda, J., Chopra, M., Matsagar, V., & Chakraborty, S. (2024). An iterative gradient descent-based reinforcement learning policy for active control of structural vibrations. Computers & Structures, 290, 107183.

## 2023

More, K., Tripura, T., Nayek, R., & Chakraborty, S. (2023). A Bayesian Framework for learning governing Partial Differential Equation from Data. Physica D - Nonlinear Phenomenon, 456, 133927.

Rashid, M. M., Chakraborty, S., & Krishnan, N. M. (2023). Revealing the Predictive Power of Neural Operators for Strain Evolution in Digital Composites. Journal of Mechanics and Physics of Solids 181, 105444.

Desai, A. S., Adhikari, S., & Chakraborty, S. (2023). Enhanced multi-fidelity modelling for digital twin and uncertainty quantification. Probabilistic Engineering Mechanics, 74, 103517.

Tripura, T. and Chakraborty, S. (2023). Robust model agnostic predictive control algorithm for randomly excited dynamical systems. Probabilistic Engineering Mechanics (in press)

Tripura, T., Hazra, B., & Chakraborty, S. (2023). Novel Girsanov correction-based Milstein schemes for analysis of nonlinear multi-dimensional stochastic dynamical systems. Applied Mathematical Modelling, 122, 350-372.

Mathpati, Y.C., More, K.S., Tripura, T., Nayek, R. and Chakraborty, S. (2023) MAntRA: A framework for model agnostic reliability analysis, Reliability Engineering & System Safety, 235, 109233.

Tripura, T., Deshai, A. A., Adhikari, S., and Chakraborty, S. (2023) Probabilistic machine learning based predictive and interpretable digital twin for dynamical systems, Computers and Structures, 281, 107008.

Rani, J., Tripura, T., Kodamana, H., Chakraborty, S., and Tamboli, P.K. (2023) Fault detection and isolation using probabilistic wavelet neural operator auto-encoder with application to dynamic processes, 173, 215-228.

Tushar, Chakraborty, S. (2023) Deep Physics Corrector: A physics enhanced deep learning architecture for solving stochastic differential equations, Journal of Computational Physics, 479, 112004.

Tripura, T., Awasthi, A., Roy, S., and Chakraborty, S. (2023) A wavelet neural operator based elastography for localization and quantification of tumors, Computer Methods and Programs in Biomedicine, 232, 107436.

Thakur, A. and Chakraborty, S. (2023), Deep Capsule Encoder-Decoder Network for Surrogate Modeling and Uncertainty Quantification, Internation Journal of Numerical Methods in Engineering, https://doi.org/10.1002/nme.7228

Tripura, T. and Chakraborty, S. (2023). Wavelet Neural Operator for solving parametric partial differential equations in computational mechanics problems, Computer Methods in Applied Mechanics and Engineering, 404, 115783.

Tripura, T and Chakraborty, S. (2023). A sparse Bayesian framework for discovering interpretable nonlinear stochastic dynamical systems with Gaussian white noise, Mechanical Systems and Signal Processing, 187, 109939.

Navaneeth N. and Chakraborty, S. (2023) Stochastic projection based approach for gradient free physics informed learning, Computer Methods in Applied Mechanics and Engineering, 406, 115842.

Garg, S. and Chakraborty, S. (2023). VB-DeepONet: A Bayesian operator learning framework for uncertainty quantification, 118, 105685.

Tripathi, U., Garg, S., Nayek, R. and Chakraborty, S. (2023) Physics integrated deep learning for uncertainty quantification and reliability estimation of nonlinear dynamical systems, Probabilistic Engineering Mechanics, 72, 103419.

Mendu, S. K. and Chakraborty, S. (2023) Gated Linear Model induced U-net for surrogate modeling and uncertainty quantification, Probabilistic Engineering Mechanics, 72, 103421.

## 2022

Padhi, A. P., Chakraborty, S., Chakrabarti, A., and Chowdhury, R. (2022). Efficient hybrid topology optimization using GPU and homogeni-zation based multigrid approach. Engineering with Computers, doi: 10.1007/s00366-022-01771-x.

Rashid, M. M., Pittie, T., Chakraborty, S., Krishnan, N. M. A (2022). Learning the Stress-Strain Fields in Digital Composites using Fourier Neural Operator, iScience, 25(11), 105452.

Navaneeth, N. and Chakraborty, S. (2022). Koopman operator for time-dependent reliability analysis. Probabilistic Engineering Mechanics, 70, 103372.

Garg, S., Gupta, H., and Chakraborty, S. (2022). Assessment of DeepONet for reliability analysis of stochastic nonlinear dynamical systems. Engineering Structures, 270, 114811.

Skandalos, K., Chakraborty, S., and Tesfamariam, S. (2022). Seismic reliability analysis using a multi-fidelity surrogate model: Example of base-isolated buildings. Structural Safety, 97, 102222.

Tripura, T., Imran, Md., Hazra, B, and Chakraborty, S. A change of measure enhanced near exact Euler Maruyama scheme for the solution to nonlinear stochastic dynamical systems. Journal of Engineering Mechanics, 148(6), 04022025.

Thakur, A. and Chakraborty, S. A deep learning based surrogate model for stochastic simulators, Probabilistic Engineering Mechanics, 68, 103248.

Garg, S., Chakraborty, S., and Hazra, B. Physics-integrated hybrid framework for model form error identification in nonlinear dynamical systems. Mechanical Engineering and System Safety, 173, 109039

Kumar, Y., Bahl, P., and Chakraborty, S. State estimation with limited sensors - A deep learning based approach. Journal of Computational Physics, 457, 111081.

Adhikari, S. and Chakraborty, S. (2022) Random matrix eigenvalue problems in structural dynamics: An iterative approach, Mechanical Systems and Signal Processing, 65, 108260.

Navaneeth, N, and Chakraborty, S. (2022). Surrogate assisted active subspace and active subspace assisted surrogate - A new paradigm for high-dimensional structural reliability analysis, Computer Methods in Applied Mechanics and Engineering, 389, 114374.

## 2021

Garg, S., Gogoi, A., Chakraborty, S., and Harza, B. (2021). Machine learning based digital twin for stochastic nonlinear multi-degree of freedom dynamical system, Probabilistic Engineering Mechanics, 66, 103173.

Chatterjee, T., Chakraborty, S., Goswami, S., Adhikari, S. and Friswell, M. I. Robust topological designs for extreme metamaterial micro-structures, Scientific Reports 11(1), 1-14.

Chakraborty, S., Das, S. and Tesfamariam, S. (2021). Robust design optimization of nonlinear energy sink under random system parameters. Probabilistic Engineering Mechanics, 65, 103139.

Dey, S., Chakraborty, S., and Tesfamariam, S. (2021) Multi-fidelity approach for uncertainty quantification of burried pipeline response undergoing fault rupture displacements in sand. Computers and Geotechnics, 136, 104197.

Chakraborty, S. and Tesfamariam, S. (2021). Subset simulation based approach for space-time-dependent system reliability analysis of corroding pipelines. Structural Safety, 90, 102073.

Martínez, E. R., Chakraborty, S. and Tesfamariam, S. (2021). Machine learning assisted stochastic-XFEM for stochastic crack propagation and reliability analysis. Theoretical and Applied Fracture Mechanics, 112, 102882.

Chakraborty, S. (2021). Transfer learning based multi-fidelity physics informed deep neural network. Journal of Computational Physics, 426, 109942.

Chakraborty, S. and Adhikari, S. (2021). Machine learning based digital twin for dynamical systems with multiple time-scales, Computers and Structures, 243, 106410.

Chakraborty, S., Adhikari, S. and Ganguli, R. (2021). The role of surrogate models in the development of digital twins for dynamic systems, Applied Mathematical Modelling, 90, 662-681.

## 2020

Das, S., Chakraborty, S., Chen, Y. and Tesfamariam, S. (2020). Robust Design Optimization for SMA Based Nonlinear Energy Sink with Negative Stiffness and Friction, Soil Dynamics and Earthquake Engineering, 140, 106466.

Mukhopadhyay, T., Naskar, S., Chakraborty, S., Karsh, P.K., Choudhury, R., and Dey, S. (2020). Stochastic Oblique Impact on Composite Laminates: A Concise Review and Characterization of the Essence of Hybrid Machine Learning Algorithms. Archives of Computational Methods in Engineering (in press)

Goswami, S., Anitescu, C., Chakraborty, S., & Rabczuk, T. (2020). Transfer learning enhanced physics informed neural network for phase-field modeling of fracture. Theoretical and Applied Fracture Mechanics, 106, 102447.

Dey, S., Chakraborty, S. and Tesfamariam, S. (2020). Structural performance of buried pipeline undergoing strike-slip fault rupture in 3D using a non-linear sand model. Soil Dynamics and Earthquake Engineering, 135, 106180.

## 2019

Karsh, P. K., Mukhopadhyay, T., Chakraborty, S., Naskar, S., & Dey, S. (2019). A hybrid stochastic sensitivity analysis for low-frequency vibration and low-velocity impact of functionally graded plates. Composites Part B: Engineering, 176, 107221.

Chakraborty, S., & Chowdhury, R. (2019). Graph-Theoretic-Approach-Assisted Gaussian Process for Nonlinear Stochastic Dynamic Analysis under Generalized Loading. Journal of Engineering Mechanics, 145(12), 04019105.

Goswami, S., Chakraborty, S., Chowdhury, R., & Rabczuk, T. (2019). Threshold shift method for reliability-based design optimization. Structural and Multidisciplinary Optimization, 1-20.

Chakraborty, S., Goswami, S., & Rabczuk, T. (2019). A surrogate assisted adaptive framework for robust topology optimization. Computer Methods in Applied Mechanics and Engineering, 346, 63-84.

Nayek, R., Chakraborty, S., & Narasimhan, S. (2019). A Gaussian process latent force model for joint input-state estimation in linear structural systems. Mechanical Systems and Signal Processing, 128, 497-530.

Mandal, B., Chakraborty, S., & Chakrabarti, A. (2019). A hybrid approach for global sensitivity analysis of FRP composite multi-bolt joints. Composite Structures, 208, 189-199.

Chatterjee, T., Chakraborty, S., & Chowdhury, R. (2019). A critical review of surrogate assisted robust design optimization. Archives of Computational Methods in Engineering, 26(1), 245-274.

## 2018

Saha, A., Chakraborty, S., Chandra, S., & Ghosh, I. (2018). Kriging based saturation flow models for traffic conditions in Indian cities. Transportation Research Part A: Policy and Practice, 118, 38-51.

Biswas, S., Chakraborty, S., Ghosh, I., & Chandra, S. (2018). Saturation flow model for signalized intersection under mixed traffic condition. Transportation Research Record, 2672(15), 55-65.

Keshtegar, B., & Chakraborty, S. (2018). Dynamical accelerated performance measure approach for efficient reliability-based design optimization with highly nonlinear probabilistic constraints. Reliability Engineering & System Safety, 178, 69-83.

Chakraborty, S., & Zabaras, N. (2018). Efficient data-driven reduced-order models for high-dimensional multiscale dynamical systems. Computer Physics Communications, 230, 70-88.

Chakraborty, S., & Majumder, D. (2018). Hybrid Reliability Analysis Framework for Reliability Analysis of Tunnels. Journal of Computing in Civil Engineering, 32(4), 04018018.

Chatterjee, T., Chakraborty, S., & Chowdhury, R. (2018). Analytical moment based approximation for robust design optimization. Structural and Multidisciplinary Optimization, 58(5), 2135-2162.

## 2017

Chakraborty, S., & Chowdhury, R. (2017). An efficient algorithm for building locally refined hp–adaptive H-PCFE: Application to uncertainty quantification. Journal of Computational Physics, 351, 59-79.

Majumder, D., Chakraborty, S., & Chowdhury, R. (2017). Probabilistic analysis of tunnels: A hybrid polynomial correlated function expansion based approach. Tunnelling and Underground Space Technology, 70, 89-104.

Chakraborty, S., & Chowdhury, R. (2017). Galerkin based generalized ANOVA for the solution of stochastic steady state diffusion problems. Probabilistic Engineering Mechanics, 50, 36-44.

Chakraborty, S., Chatterjee, T., Chowdhury, R., & Adhikari, S. (2017). A surrogate based multi-fidelity approach for robust design optimization. Applied Mathematical Modelling, 47, 726-744.

Chakraborty, S., & Chowdhury, R. (2017). Towards ‘hp adaptive’generalized ANOVA. Computer Methods in Applied Mechanics and Engineering, 320, 558-581.

Chakraborty, S., & Chowdhury, R. (2017). A hybrid approach for global sensitivity analysis. Reliability Engineering & System Safety, 158, 50-57.

Chakraborty, S., & Chowdhury, R. (2017). Hybrid framework for the estimation of rare failure event probability. Journal of Engineering Mechanics, 143(5), 04017010.

Mukhopadhyay, T., Chakraborty, S., Dey, S., Adhikari, S., & Chowdhury, R. (2017). A critical assessment of Kriging model variants for high-fidelity uncertainty quantification in dynamics of composite shells. Archives of Computational Methods in Engineering, 24(3), 495-518.

## 2016

Biswas, S., Chakraborty, S., Chandra, S., & Ghosh, I. (2016). Kriging-based approach for estimation of vehicular speed and passenger car units on an urban arterial. Journal of Transportation Engineering, Part A: Systems, 143(3), 04016013.

Chakraborty, S., & Chowdhury, R. (2016). Modelling uncertainty in incompressible flow simulation using Galerkin based generalized ANOVA. Computer Physics Communications, 208, 73-91.

Chakraborty, S., & Chowdhury, R. (2016). Sequential experimental design based generalised ANOVA. Journal of Computational Physics, 317, 15-32.

Chakraborty, S., & Chowdhury, R. (2016). Moment independent sensitivity analysis: H-PCFE–based approach. Journal of Computing in Civil Engineering, 31(1), 06016001.

Chakraborty, S., & Chowdhury, R. (2016). Assessment of polynomial correlated function expansion for high-fidelity structural reliability analysis. Structural Safety, 59, 9-19.

Chakraborty, S., Chatterjee, T., Chowdhury, R., & Adhikari, S. (2017). Robust design optimization for crashworthiness of vehicle side impact. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 3(3), 031002.

## 2015

Chakraborty, S., & Chowdhury, R. (2015). Multivariate function approximations using the D-MORPH algorithm. Applied Mathematical Modelling, 39(23-24), 7155-7180.

Chakraborty, S., & Chowdhury, R. (2015). A semi-analytical framework for structural reliability analysis. Computer Methods in Applied Mechanics and Engineering, 289, 475-497.

Chakraborty, S., Mandal, B., Chowdhury, R., & Chakrabarti, A. (2016). Stochastic free vibration analysis of laminated composite plates using polynomial correlated function expansion. Composite Structures, 135, 236-249.

Chatterjee, T., Chakraborty, S., & Chowdhury, R. (2016). A bi-level approximation tool for the computation of FRFs in stochastic dynamic systems. Mechanical Systems and Signal Processing, 70, 484-505.

## 2014

Madke, R. R., Chakraborty, S., & Chowdhury, R. (2014). Multiscale approach for the nonlinear behavior of cementitious composite. Computational Materials Science, 93, 29-35.

Chakraborty, S., & Chowdhury, R. (2014). Polynomial correlated function expansion for nonlinear stochastic dynamic analysis. Journal of Engineering Mechanics, 141(3), 04014132.

Chakraborty, S., Dalui, S. K., & Ahuja, A. K. (2014). Experimental Investigation of Surface Pressure on (+) Plan Shape Tall Building. Jordan Journal of Civil Engineering, 159(3147), 1-12.

Mukherjee, S., Chakraborty, S., Dalui, S. K., & Ahuja, A. K. (2014). Wind induced pressure on'Y'plan shape tall building. Wind and Structures, 19(5), 523-540.

Chakraborty, S., Dalui, S. K., & Ahuja, A. K. (2014). Wind load on irregular plan shaped tall building–a case study. Wind and Structures, 19(1), 59-73.

## 2013

Chakrabortya, S., Dalui, S. K., & Ahuja, A. K. (2013). Experimental and numerical study of surface pressure on ‘+’plan shape tall building. International Journal of Construction Materials and Structures, 1, 45-48.