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publications
Dynamic Characteristics of Submicron Particle Deposited on the Charged Spherical Collector
Authors: Abhishek Srivastava, Bahni Ray, Mayank Kumar, Debabrata Dasgupta, Rochish Thaokar, Y. S. Mayya
Numerical investigation of the dynamic characteristics of oppositely charged submicron particles approaching a charged spherical collector under a non-uniform electric field.
Electrostatic Scrubbing Charged Spherical Collector Particle Dynamics Submicron Particles Aerosol Collection Electrostatic Forces COMSOL Multiphysics CFD
Influence of Non-Uniform Electric Field Due to Spherical Collector on Particle Dynamics in Quiescent Air
Authors: Abhishek Srivastava, Debabrat Biswal, Bahni Ray, Mayank Kumar, Debabrata Dasgupta
Experimental and numerical investigation of charged particle dynamics in the non-uniform electric field of a spherical collector, providing insights into particle trajectories, energy budgets, and deposition behavior.
Aerosol Filtration Particle Deposition Electrostatics Charged Collector Computational Fluid Dynamics Particle Dynamics
Analyzing the impact of charge particle rebound on spherical collector efficiency via explainable machine learning
Authors: Abhishek Srivastava, Sagar Kandpal, Bahni Ray, Mayank Kumar, Debabrata Dasgupta
A physics-informed and interpretable machine learning framework integrating 3D Lagrangian particle tracking, LightGBM, SHAP, and Partial Dependence Plots (PDP) to analyze charged particle collection efficiency and rebound dynamics in electrostatic precipitators.
Charged Particle Collection Non uniform electric field Machine Learning Explainable AI Shapley Additive Explanations Particle Dynamics
Machine learning-based regime identification and size-resolved optimization of charged-particle capture and rebound on spherical collectors
Authors: Abhishek Srivastava, Sagar Kandpal, Bahni Ray, Mayank Kumar, Debabrata Dasgupta
A clustering-guided surrogate-assisted optimization framework integrating LightGBM surrogate modeling, K-means clustering, NSGA-II, TOPSIS, and explainable artificial intelligence to optimize charged particle collection while minimizing particle rebound.
Scientific Machine Learning Surrogate Modeling Multi-objective Optimization NSGA-II K-means Clustering Explainable Artificial Intelligence SHAP Electrostatic Precipitators Particle Rebound TOPSIS
Conditioning-dependent variational inference for uncertainty quantification in physics-based inverse problems
Authors: Abhishek Srivastava, Arijit Hazra, R. Dubaku
Variational-autoencoder-based posterior approximation for Bayesian inverse problems in heat conduction, benchmarked against MCMC reference posteriors, with a Fisher-Information-Matrix analysis explaining when amortized variational posteriors under- or over-estimate uncertainty.
Bayesian Inverse Problems Variational Inference Variational Autoencoders Uncertainty Quantification Fisher Information Matrix MCMC
research
Multiphysics CFD Modeling of Aerosol Particle Capture by Charged Spherical Collector
Development of a high-fidelity multiphysics computational framework integrating computational fluid dynamics, electrostatics, and particle tracing to investigate charged aerosol transport and optimize electrostatic particle collection by charge spherical collector.
Explainable Artificial Intelligence Framework for Electrostatic Particle Collection
Developed an interpretable machine learning framework integrating LightGBM, SHAP, and Partial Dependence Plots (PDP) to analyze charged particle rebound, identify dominant physical mechanisms, and optimize electrostatic particle collection.
Clustering-Guided Surrogate-Assisted Multi-Objective Optimization of Electrostatic Particle Collection
Developed a clustering-guided surrogate-assisted optimization framework integrating machine learning, K-means clustering, NSGA-II, TOPSIS, and explainable AI to optimize electrostatic particle collection while minimizing particle rebound.
Solving Bayesian Inverse Problems using Variational Autoencoders for Uncertainty Quantification
Developing Bayesian deep learning frameworks for inverse problems by integrating variational inference, deep generative models, Fisher information geometry, and uncertainty quantification for computational science and engineering.
skills
teaching
Teaching Assistant — Intermediate Heat Transfer
, , 1900
Course
Teaching Assistant — Advanced Thermodynamics
, , 1900