First CCE MathWorks Research Prize Awarded to Mojtaba Forghani Oozroody

We’re pleased to announce Computational Science and Engineering (CSE) PhD student Mojtaba Forghani Oozroody has been selected as the inaugural winner of the MIT Center for Computational Engineering MathWorks Prize for Outstanding Doctoral Research, Computational Science and Engineering Program, 2018. Mojtaba is a student in the Department of Mechanical Engineering working with Professor Nicolas Hadjiconstantinou a problem involving multiscale computation, inverse problem solution and optimization.

A brief abstract of Mojtaba’s work is included below:

Micro/nano-scale solid-state heat transfer plays an important role in a number of important applications of practical interest‎, ‎such as the design and fabrication of nano-electronic devices‎, ‎thermoelectric materials, ‎biosensors‎, ‎drug delivery systems‎, ‎etc‎. ‎At the same time‎, ‎modeling nanoscale heat transfer presents a number of scientific challenges‎, ‎primarily because the convenient macroscopic description based on the Fourier heat conduction equation is no longer valid at these scales‎. ‎As a result‎, ‎alternative‎, ‎higher fidelity descriptions have been developed‎. ‎In order to capture the effects neglected by the Fourier description‎, ‎these models treat matter at the sub-continuum level (e.g‎. ‎molecular‎, ‎coarse-grained molecular‎, ‎kinetic) and thus require knowledge of material properties at a finer level than the continuum‎. ‎While these properties can‎, ‎in principle‎, ‎be predicted using computational methods such as density functional theory (DFT) and molecular dynamics (MD)‎, ‎due to the high computational cost of these approaches and the approximations associated with their potential energy surface (PES) functions‎, ‎experimental approaches remain very valuable‎. ‎However‎, ‎the inverse problem of extracting these material properties from so called thermal spectroscopy experiments ‎(TSEs) remains an open problem.‎ ‎We have developed mathematical/computational formulations that provide solutions to the inverse problem of extracting this sub-continuum information through an optimization framework which aims at minimizing the discrepancies between the theoretical prediction of thermal behavior based on Monte Carlo (MC) simulations of the Boltzmann transport equation (BTE) and TSEs, ‎which record the thermal response of the material of interest to some judiciously chosen thermal excitation‎