TC
Tanmoy Chatterjee
University of Surrey, Swansea University, Indian Institute of Technology Roorkee, Indian Institute of Engineering Science and Technology, M/s. Paritosh Bhowmick & Co.
0000-0002-9374-7992
About
My research interests include, uncertainty quantification, robust design optimization, reliability analysis, dynamic sub-structuring, mechanics and wave propagation of metamaterials, topology optimization, digital twins, and surrogate modelling using machine learning.
Employment
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University of Surrey Lecturer in Resilient Design2022 - Present
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Swansea University Research Assistant2018 - 2022
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M/s. Paritosh Bhowmick & Co. Structural Coordinator2013 - 2014
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M/s. Paritosh Bhowmick & Co. Graduate Engineer Trainee2010 - 2011
Education
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Indian Institute of Technology Roorkee Doctor of Philosophy2014 - 2018
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Indian Institute of Engineering Science and Technology Master of Engineering2011 - 2013
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Meghnad Saha Institute of Technology B.Tech2006 - 2010
Projects & Funding
Projects & funding information is unavailable.
Publications (49)
- Automated machine learning-based predictive models for multi-hazard catastrophic risk assessment in offshore wind turbines Save
- Machine Learning-Based Predictive Models for Multi-Hazard Response Analysis of Offshore Wind Turbines Save
- Stochastic nonlinear model updating in structural dynamics using a novel likelihood function within the Bayesian-MCMC framework Save
- Stochastic dispersion behavior and optimal design of locally resonant metamaterial nanobeams using nonlocal strain gradient theory Save
- Modelling and stochastic updating of nonlinear structural joints Save
- A physics-informed neural network enhanced importance sampling (PINN-IS) for data-free reliability analysis Save
- MATLAB Implementation of Physics Informed Deep Neural Networks for Forward and Inverse Structural Vibration Problems Save
- Sparse identification of quasi-zero stiffness dynamics Save
- Exploring CNN based architectures for structure to property mapping of 2D micro-architected materials Save
- Data-driven system identification of unknown systems utilising sparse identification of nonlinear dynamics (SINDy) Save