SOUVIK CHAKRABORTY

About

I am currently working as an Associate Professor at the Department of Applied Mechanics, IIT Delhi. My primary research focus is at the intersection of Scientific Computing and Machine Learning where the objective is to develop novel tools for solving a wide array of problems in computational mechanics

Employment

• Indian Institute of Technology Delhi Associate Professor
  2025 - Present
• Indian Institute of Technology Delhi Assistant Professor
  2020 - 2025
• University of British Columbia Postdoctoral research associate
  2019 - 2019
• University of Notre Dame Post doctoral research associate
  2017 - 2019
• Indian Institute of Technology Roorkee Research Asscoaite
  2016 - 2017

Education

• Indian Institute of Technology Roorkee Ph.D
  2013 - Present
• Bengal Engineering and Science University Master of Engineering
  2011 - 2013
• NIT Durgapur B.Tech
  2006 - 2010

Projects & Funding

Projects & funding information is unavailable.

Publications (55)

• Multiresolution Fusion Network with Attention for Brain and Breast Cancer Detection
  2027 DOI: 10.1007/978-3-032-30813-9_34
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• Hardware–Software Co-Design for Event-Driven SNN Deployment on Low-Cost Neuromorphic FPGAs
  2026 DOI: 10.1145/3822454.3822493
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• Neural combinatorial wavelet neural operator for catastrophic forgetting free in-context operator learning of multiple partial differential equations
  Computer Physics Communications 2026 DOI: 10.1016/j.cpc.2025.109882
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• Scalable h-adaptive probabilistic solver for time-independent and time-dependent systems
  Computer Methods in Applied Mechanics and Engineering 2026 DOI: 10.1016/j.cma.2025.118647
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• A dual physics-informed neural network for topology optimization
  Journal of Computational Physics 2026 DOI: 10.1016/j.jcp.2026.114666
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• From local interactions to global operators: Scalable Gaussian process operator for physical systems
  Journal of Computational Physics 2026 DOI: 10.1016/j.jcp.2026.114785
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• Neuroscience Inspired Graph Operators Towards Edge-Deployable Virtual Sensing for Irregular Geometries
  2026 DOI: 10.1145/3822454.3822494
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• Learning to predict and control with sparse model discovery and deep temporal difference reinforcement learning
  Mechanical Systems and Signal Processing 2026 DOI: 10.1016/j.ymssp.2026.114226
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• A Bayesian approach for discovering time- delayed differential equation from data
  Journal of Computational Physics 2025 DOI: 10.1016/j.jcp.2025.114064
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• Energy network for state estimation with random sensors and sparse labels
  Computer Physics Communications 2025 DOI: 10.1016/j.cpc.2025.109566
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