Book Chapters
T. Tripura, S. Garg, and S. Chakraborty, “Digital twin for dynamical systems,” in Machine Learning in Modeling and Simulation: Methods and Applications, Springer, 2023, pp. 255–296. [Link]
Submitted Manuscripts
T. Tripura and S. Chakraborty, Learning to predict and control with sparse model discovery and deep temporal difference reinforcement learning.
T. Tripura and S. Chakraborty, Flowmarl: Model-agnostic reinforcement learning for active flow control.
Published Articles
2025
T. Tripura and S. Chakraborty, “Neural combinatorial wavelet neural operator for catastrophic forgetting free in-context operator learning of multiple partial differential equations,” Computer Physics Communications, p. 109 882, 2025. [Paper]
H. Soin, T. Tripura, and S. Chakraborty, “Generative flow induced neural architecture search: Towards discovering optimal architecture in wavelet neural operator,” Computer Physics Communications, p. 109 755, 2025. [Paper]
J. Rani, T. Tripura, H. Kodamana, and S. Chakraborty, “Generative adversarial wavelet neural operator with applications to fault detection and isolation of multivariate time series data,” Control Engineering Practice, vol. 165, p. 106 548, 2025. [Paper]
R. Kumar, T. Tripura, S. Chakraborty, and S. Roy, “Deep muscle electromyogram construction using a physics-integrated deep learning approach,” Engineering Applications of Artificial Intelligence, vol. 159, p. 111 613, 2025. [Paper]
2024
T. Tripura, A. Thakur, and S. Chakraborty, “Multi-fidelity wavelet neural operator surrogate model for time-independent and time-dependent reliability analysis,” Probabilistic Engineering Mechanics, vol. 77, p. 103 672, 2024. [Paper]
T. Tripura and S. Chakraborty, “Discovering interpretable lagrangian of dynamical systems from data,” Computer Physics Communications, vol. 294, p. 108 960, 2024. [Paper]
N. Navaneeth, T. Tripura, and S. Chakraborty, “Physics informed wno,” Computer Methods in Applied Mechanics and Engineering, vol. 418, p. 116 546, 2024. [Paper]
T. Tripura and S. Chakraborty, “A bayesian framework for discovering interpretable lagrangian of dynamical systems from data,” Mechanical Systems and Signal Processing, vol. 221, p. 111 737, 2024. [Paper]
T. Tripura, S. Panda, B. Hazra, and S. Chakraborty, “Data-driven discovery of interpretable lagrangian of stochastically excited dynamical systems,” Computer Methods in Applied Mechanics and Engineering, vol. 427, p. 117 032, 2024.. [Paper]
Q. Cao, S. Goswami, T. Tripura, S. Chakraborty, and G. E. Karniadakis, “Deep neural operators can predict the real-time response of floating offshore structures under irregular waves,” Computers & Structures, vol. 291, p. 107 228, 2024. [Paper]
Y. C. Mathpati, T. Tripura, R. Nayek, and S. Chakraborty, “Discovering stochastic partial differential equations from limited data using variational bayes inference,” Computer Methods in Applied Mechanics and Engineering, vol. 418, p. 116 512, 2024. [Paper]
2023
T. Tripura and S. Chakraborty, “Wavelet neural operator for solving parametric partial differential equations in computational mechanics problems,” Computer Methods in Applied Mechanics and Engineering, vol. 404, p. 115 783, 2023. [Paper]
T. Tripura, A. Awasthi, S. Roy, and S. Chakraborty, “A wavelet neural operator based elastography for localization and quantification of tumors,” Computer Methods and Programs in Biomedicine, vol. 232, p. 107 436, 2023. [Paper]
T. Tripura and S. Chakraborty, “A sparse bayesian framework for discovering interpretable nonlinear stochastic dynamical systems with gaussian white noise,” Mechanical Systems and Signal Processing, vol. 187, p. 109 939, 2023. [Paper]
T. Tripura and S. Chakraborty, “Robust model agnostic predictive control algorithm for randomly excited dynamical systems,” Probabilistic Engineering Mechanics, vol. 74, p. 103 517, 2023. [Paper]
T. Tripura, A. S. Desai, S. Adhikari, and S. Chakraborty, “Probabilistic machine learning based predictive and interpretable digital twin for dynamical systems,” Computers & Structures, vol. 281, p. 107 008, 2023. [Paper]
J. Rani, T. Tripura, H. Kodamana, S. Chakraborty, and P. K. Tamboli, “Fault detection and isolation using probabilistic wavelet neural operator auto-encoder with application to dynamic processes,” Process Safety and Environmental Protection, vol. 173, pp. 215–228, 2023. [Paper]
Y. C. Mathpati, K. S. More, T. Tripura, R. Nayek, and S. Chakraborty, “Mantra: A framework for model agnostic reliability analysis,” Reliability Engineering & System Safety, vol. 235, p. 109 233, 2023. [Paper]
K. S. More, T. Tripura, R. Nayek, and S. Chakraborty, “A bayesian framework for learning governing partial differential equation from data,” Physica D: Nonlinear Phenomena, vol. 456, p. 133 927, 2023. [Paper]
T. Tripura, B. Hazra, and S. Chakraborty, “Novel girsanov correction based milstein schemes for analysis of nonlinear multi-dimensional stochastic dynamical systems,” Applied Mathematical Modelling, vol. 122, pp. 350–372, 2023. [Paper]
2022
T. Tripura, M. Imran, B. Hazra, and S. Chakraborty, “Change of measure enhanced near-exact Euler–Maruyama scheme for the solution to nonlinear stochastic dynamical systems,” Journal of Engineering Mechanics, vol. 148, no. 6, p. 04 022 025, 2022. [Paper]
S. Panda, T. Tripura, and B. Hazra, “Online damage detection of earthquake-excited structure based on near real-time envelope extraction,” Structural Health Monitoring, vol. 21, no. 2, pp. 298–319, 2022. [Paper]
2021
S. Panda, T. Tripura, and B. Hazra, “First-order error-adapted eigen perturbation for real-time modal identification of vibrating structures,” Journal of Vibration and Acoustics, vol. 143, no. 5, p. 051 001, 2021. [Paper]
2020
T. Tripura, B. Bhowmik, V. Pakrashi, and B. Hazra, “Real-time damage detection of degrading systems,” Structural Health Monitoring, vol. 19, no. 3, pp. 810–837, 2020. [Paper]
T. Tripura, A. Gogoi, and B. Hazra, “An ito–taylor weak 3.0 method for stochastic dynamics of nonlinear systems,” Applied Mathematical Modelling, vol. 86, pp. 115–141, 2020. [Paper]
B. Bhowmik, T. Tripura, B. Hazra, and V. Pakrashi, “Real time structural modal identification using recursive canonical correlation analysis and application towards online structural damage detection,” Journal of Sound and Vibration, vol. 468, p. 115 101, 2020. [Paper]
B. Bhowmik, T. Tripura, B. Hazra, and V. Pakrashi, “Robust linear and nonlinear structural damage detection using recursive canonical correlation analysis,” Mechanical Systems and Signal Processing, vol. 136, p. 106 499, 2020. [Paper]
2019
B. Bhowmik, T. Tripura, B. Hazra, and V. Pakrashi, “First-order eigen-perturbation techniques for real-time damage detection of vibrating systems: Theory and applications,” Applied Mechanics Reviews, vol. 71, no. 6, p. 060 801, 2019. [Paper]
Conference Proceedings
T. Tripura and S. Chakraborty, “Predictive and interpretable digital twin for dynamical systems,” in 68th Congress of the Indian Society of Theoretical and Applied Mechanics (ISTAM 2024), NIT Warangal, 2024.
T. Tripura, N. N N, and S. Chakraborty, “Physics-informed wavelet neural operator for data-free learning of parametric partial differential equations,” in 9th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS 2024), Lisboa, Portugal, 2024.
T. Tripura and S. Chakraborty, “Sparse system identification of dynamical systems from output-only measurements,” in 9th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COMPDYN 2023), Tripura, Tapas and Chakraborty, Souvik, 2023.
T. Tripura, S. Kumar, and S. Chakraborty, “Wavelet neural operator for reliability analysis,” in 9th International Congress on Computational Mechanics and Simulations (ICCMS 2023), IIT Gandhinagar, Gujarat, 2023.
T. Tripura, B. Hazra, and S. Chakraborty, “Near-exact Euler-Maruyama for solving nonlinear systems in stochastic dynamics,” in 5th Indian Conference on Applied Mechanics (INCAM 2022), NIT Jamshedpur, 2022.
B. Bhowmik, T. Tripura, B. Hazra, and V. Pakrashi, “Damage detection under progressive operational degradation of structures in real time,” in 8th International Operational Modal Analysis Conference (IOMAC 2019), 2019, pp. 137–145.