Publications

Yoo, J. P., Garg, S., Chakraborty, S., & Alam, S. B. (2026). When spike sparsity does not translate to deployed cost: VS-WNO on Jetson Orin Nano. arXiv preprint arXiv:2604.17040.

Howes, W., Ahmed, F., Kobayashi, K., Chakraborty, S., & Alam, S. B. (2026). Neuroscience inspired graph operators towards edge-deployable virtual sensing for irregular geometries. arXiv preprint arXiv:2604.16722.

Roy, S., Chakraborty, S., & Alam, S. B. (2026). Beyond uniform sampling: Synergistic active learning and input denoising for robust neural operators. arXiv preprint arXiv:2604.13316.

Roy, S., Chakraborty, S., Rizwan-uddin, & Alam, S. B. (2026). SCNO: Spiking compositional neural operator—Towards a neuromorphic foundation model for nuclear PDE solving. arXiv preprint arXiv:2604.11625.

Ballester, M., Lopez-Tapia, S., Gossage, S., Koller, P., Srivastava, P. M., Demir, U., Jo, Y., Marquez, A. P., Wuersch, C., Chakraborty, S., Kalogera, V., & Katsaggelos, A. (2026). Learning the stellar structure equations via self-supervised physics-informed neural networks. arXiv preprint arXiv:2604.06255.

Howes, W., Yoo, J., Kobayashi, K., Sarkar, S., Ahmed, F., Chakraborty, S., & Alam, S. B. (2026). Real-time sensing of inaccessible physical fields via an edge-deployable hardware-portable graph neural operator. arXiv preprint arXiv:2604.01802.

Roy, S., Kobayashi, K., Chakraborty, S., Rizwan-uddin, & Alam, S. B. (2026). Adversarial vulnerabilities in neural operator digital twins: Gradient-free attacks on nuclear thermal-hydraulic surrogates. arXiv preprint arXiv:2603.22525.

Garg, S., & Chakraborty, S. (2026). CoNBONet: Conformalized neuroscience-inspired Bayesian operator network for reliability analysis. arXiv preprint arXiv:2603.21678.

Garg, S., Mandl, L., Goswami, S., & Chakraborty, S. (2026). SPINONet: Scalable spiking physics-informed neural operator for computational mechanics applications. arXiv preprint arXiv:2603.21674.

Kumar, V., Alam, S. B., & Chakraborty, S. (2026). TrustFed: Enabling trustworthy medical AI under data privacy constraints. arXiv preprint arXiv:2603.21656.

Puthli, A., Goswami, S., & Chakraborty, S. (2026). Neural Hodge corrective solvers: A hybrid iterative-neural framework. arXiv preprint arXiv:2602.03404.

Kumar, V., & Chakraborty, S. (2026). CortiNet: A physics-perception hybrid cortical-inspired dual-stream network for gallbladder disease diagnosis from ultrasound. arXiv preprint arXiv:2602.01000.

Garg, S., & Chakraborty, S. (2025). Event-driven physics-informed operator learning for reliability analysis. arXiv preprint arXiv:2511.06083.

Roy, A., & Chakraborty, S. (2025). Competition is the key: A game theoretic causal discovery approach. arXiv preprint arXiv:2510.20106.

Yoo, J. P., Kobayashi, K., Chakraborty, S., & Alam, S. B. (2025). Sensing without colocation: Operator-based virtual instrumentation for domains beyond physical reach. arXiv preprint arXiv:2510.18041.

Kobayashi, K., Garg, S., Ahmed, F., Chakraborty, S., & Alam, S. B. (2025). Distribution-free uncertainty-aware virtual sensing via conformalized neural operators. arXiv preprint arXiv:2507.11574.

Garg, S., & Chakraborty, S. (2026). NeuroPINNs: Neuroscience-inspired Physics-informed Neural Networks. Journal of Computational Physics, 115161.

Sarkar, S., & Chakraborty, S. (2026). Physics- and geometry-aware spatio-spectral graph neural operator for time-independent and time-dependent PDEs. Journal of Computational Physics, 562, 115029.

Tripura, T., & Chakraborty, S. (2026). Learning to predict and control with sparse model discovery and deep temporal difference reinforcement learning. Mechanical Systems and Signal Processing, 251, 114226.

Kumar, S., & Chakraborty, S. (2026). Scalable random wavelet features: Efficient non-stationary kernel approximation with convergence guarantees. International Conference on Learning Representations (ICLR).

Kanwar, A., Soin, H., Barnawal, A., Chopra, M., Vagadia, H., Banerjee, T., Tuli, S., Paul, R., & Chakraborty, S. (2026). PhyPlan: Learning to plan tasks with generalizable and rapid physical reasoning for embodied manipulation. Association for the Advancement of Artificial Intelligence (AAAI), Main Track.

Thakur, A., Kumar, S., Zahr, M., & Chakraborty, S. (2026). Scalable h-adaptive probabilistic solver for time-independent and time-dependent systems. Computer Methods in Applied Mechanics and Engineering, 450, 118647.

Kumar, S., Tripura, T., Nayek, R., & Chakraborty, S. (2026). From local interactions to global operators: Scalable Gaussian process operator for physical systems. Journal of Computational Physics, 556, 114785.

Thakur, A., & Chakraborty, S. (2026). Mixture density nonlinear manifold decoder for emulating stochastic simulators and one-shot uncertainty propagation. Journal of Engineering Mechanics, 152(2), 04025101.

Singh, A., Chakraborty, S., & Chowdhury, R. (2026). A dual physics-informed neural network for topology optimization. Journal of Computational Physics, 551, 114666.

Soin, H., Tripura, T., & Chakraborty, S. (2025). Generative flow induced neural architecture search: Towards discovering optimal architecture in wavelet neural operator. Computer Physics Communications, 316, 109755.

Rani, J., Tripura, T., Kodamana, H., & Chakraborty, S. (2025). Generative adversarial wavelet neural operator with applications to fault detection and isolation of multivariate time series data. Control Engineering Practice, 165, 106548.

Kumar, R., Tripura, T., Chakraborty, S., & Roy, S. (2025). Deep muscle electromyogram construction using a physics-integrated deep learning approach. Engineering Applications of Artificial Intelligence, 159, 111613.

Tripura, T., & Chakraborty, S. (2025). Neural Combinatorial Wavelet Neural Operator for catastrophic forgetting free in-context operator learning of multiple partial differential equations. Computer Physics Communications, 109882.

Garg, S., & Chakraborty, S. (2025). Distribution-free uncertainty quantification for neuroscience-inspired deep neural operators. Journal of Computational Physics, 534, 114012.

Chowdhury, D., & Chakraborty, S. (2025). A Bayesian approach for discovering time-delayed differential equation from data. Journal of Computational Physics, 536, 114064.

Navaneeth, N., & Chakraborty, S. (2025). Geometry adaptive waveformer for cardio-vascular modeling. Computers in Biology and Medicine, 190, 110069.

Tushar, & Chakraborty, S. (2025). Differentiable physics augmented wavelet neural operator: A gray box model for a class of stochastic mechanics problem. Probabilistic Engineering Mechanics, 80, 103760.

Jain, I., Garg, S., Shriyam, S., & Chakraborty, S. (2025). Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning. Journal of the Mechanics and Physics of Solids, 200, 106152.

Kumar, Y., & Chakraborty, S. (2025). Energy network for state estimation with random sensors and sparse labels. Computer Physics Communications, 311, 109566.

Panda, S., Chakraborty, S., & Hazra, B. (2025). A general framework for symplectic geometric integration for stochastically excited Hamiltonian systems on manifolds. International Journal of Non-Linear Mechanics, 170, 105001.

Kumar, Y., Sarkar, S., & Chakraborty, S. (2025). GrADE: A graph based data-driven solver for time-dependent nonlinear partial differential equations. Machine Learning for Computational Science and Engineering, 1(1), 1–18.

Kumar, S., Nayek, R., & Chakraborty, S. (2025). Towards Gaussian process for operator learning: An uncertainty-aware resolution-independent operator learning algorithm for computational mechanics. Computer Methods in Applied Mechanics and Engineering, 435, 117664.

Sarkar, S., & Chakraborty, S. (2025). Spatio-spectral graph neural operator for solving computational mechanics problems on irregular domain and unstructured grid. Computer Methods in Applied Mechanics and Engineering, 435, 117659.

Garg, S., & Chakraborty, S. (2025). Randomized prior wavelet neural operator for uncertainty quantification. Probabilistic Engineering Mechanics, 81, 103817.

Navaneeth, N., Tushar, & Chakraborty, S. (2025). Harnessing physics-informed operators for high-dimensional reliability analysis problems. Probabilistic Engineering Mechanics, 81, 103807.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.