Rimple joined NREL's wind energy science group as a postdoctoral researcher in 2020. His research focuses on applying modern machine learning and uncertainty quantification (UQ) tools to advance sustainable renewable-energy development. Prior to joining NREL, he was pursuing his doctoral research where he focused on designing efficient Bayesian algorithms using high-performance computing to execute probabilistic modeling of real-life engineering systems. In particular, he was extensively involved in advancing and implementing UQ algorithms such as Bayesian model updating, sensitivity analysis, Markov Chain Monte Carlo sampling, Kalman filtering, Sparse learning, and Bayesian model comparison.

Research Interests

Uncertainty quantification

Bayesian methods

Stochastic simulation

Nonlinear filtering

Computational mechanics

Fluid-structure interactions

Education

Ph.D., Civil Engineering, Carleton University, Canada

M.S., Civil Engineering, Carleton University, Canada

B.S., Civil Engineering, Indian Institute of Technology Bombay, India

Featured Work

Bayesian Model Selection Using Automatic Relevance Determination for Nonlinear Dynamical Systems, Computer Methods in Applied Mechanics and Engineering (2017)

Bayesian Inference of Nonlinear Unsteady Aerodynamics From Aeroelastic Limit Cycle Oscillations, Journal of Computational Physics (2016)

Bayesian Model Selection for Nonlinear Aeroelastic Systems Using Wind-Tunnel Data, Computer Methods in Applied Mechanics and Engineering (2014)

Awards and Honors

Senate medal for outstanding academic achievement, Ph.D. (2020)