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Manav Vohra

Research Assistant Professor of Civil and Environmental Engineering


Civil and Environmental Engineering


Intellectual Neighborhoods

Research Focus

Computational models are widely used to mimic and potentially predict physical phenomena. However, modeling assumptions, numerical approximations, parametric uncertainties as well as observational errors in data used to calibrate the models contribute to the discrepancy between predictions and reality. My research aims to develop computational approaches as well as implement state-of-the-art techniques for model calibration, validation, and quantification of uncertainty associated with model predictions. I am further interested in the development of efficient strategies for characterizing the inadequacy in model formulations for reliable decision-making in a scenario where observations may not be available. This is remarkably challenging as the discrepancy between predictions and reality typically needs to be formulated based upon the physics of the problem especially when conventional techniques such as those involving Gaussian processes might fail to satisfy conservation laws.

Dr. Vohra’s on-going research at Vanderbilt focuses on the development of a framework for uncertainty quantification in a multi-scale, multi-fidelity setting. Applications include predictions of thermal properties of material systems at micro-scales using approaches such as Molecular Dynamics, Boltzmann Transport Equation, and Quasi Monte Carlo.

After graduating with an MS from Johns Hopkins University in 2012 and a PhD from Duke University in 2015, Dr. Vohra pursued post-doctoral research at Corning Incorporated, NY and the Institute for Computational Engineering and Sciences (ICES) at the University of Texas at Austin. His doctoral research aimed at the development and calibration of physics-based models for energetic nano-composites. At UT, his work focused on the assessment of inadequacy in model formulations for contaminant transport in a porous media, and algorithm development for inference on manifolds in a Bayesian setting.

Biography

Vohra has served two years as a postdoc, one at Corning, Inc. and the other at the University of Texas at Austin. He has strong research experience in uncertainty quantification and experimental design, with applications in fluid mechanics and materials. He also has a strong background in heat transfer and numerical methods. He is working with Sankaran Mahadevan, John R. Murray Sr. Chair in Engineering, on his research in civil and environmental engineering.

Selected Publications

1. M. Vohra, A.Alexanderian, C. Safta, S. Mahadevan (2019) “Sensitivity-driven adaptive construction of reduced-space surrogates.”, Journal of Scientific Computing (in press).

2. M. Vohra, S. Mahadevan (2018) “Discovering the Active Subspace for Efficient UQ of Molecular Dynamics Simulations of Phonon Transport in Silicon.”, International Journal of Heat and Mass Transfer, Vol. 132, pp. 577-586.

3. M. Vohra, A.Y. Nobakht, S. Shin, S. Mahadevan (2018) “Uncertainty Quantification in Non-Equilibrium Molecular Dynamics Simulations of Thermal Transport.”, International Journal of Heat and Mass Transfer, Vol. 127, Part B, pp. 297-307.

4. M. Vohra, X. Huan, T.P. Weihs, O.M. Knio (2017) “ Design Analysis for Optimal Calibration of Diffusivity in Reactive Multilayers.”, Combustion Theory and Modelling, pp. 1–27.

5. M. Vohra, T.P. Weihs, O.M. Knio (2014) “A simplified computational model of the oxidation of Zr/Al multilayers”, Combustion and Flame. Vol. 162, No. 1, pp. 249-257.

Notable Awards

1. Mahato Memorial Fellowship: Awarded in December 2013 by the Pratt School of Engineering at Duke University in recognition to excellence in Graduate research.
2. MEMS Oral Presentation Award: Received in August 2013 from the Mechanical Engineering and Materials Science department at Duke University.
3. NSF Fellowship: Awarded in August 2012 in recognition to his modeling contributions in the area of nanocalorimetry.
4. University Gold Medal: Awarded by IIT Dhanbad for best academic performance in the Mechanical Engineering class of 2010.