Sicheng He is a postdoc associate at the Department of Aeronautics and Astronautics, MIT.
He is currently working on a project with FAA to improve the guideline for aircraft noise and emission standards.
His research interest is applying multidisciplinary design optimization (MDO) to come up with a better design / developing MDO methods to enable its application to problems never solved before.
His previous research includes off-shore wind turbine design optimization, aircraft flutter suppression, aerodynamic shape optimization with laminar-turbulent transition mechanism, aerodynamic shape optimization using machine learning, and convex programming-based novel structural optimization algorithm development.
Recently, he has been working on methods to include more sophisticated dynamical system models (e.g. bifurcation and LCO) and control as a discipline in MDO (also known as control co-design).
His research on flutter and limit cycle oscillation has been awarded a best student paper prize (2nd place) of [2019 AIAA Aviation’s MDO Student Paper Competition].
And his research on the gradient-enhanced neural networks is the engine of (http://webfoil.engin.umich.edu/).
Wind turbine and aircraft aerostructural optimization
Flutter and limit cycle oscillation suppression
Matrix-free Newton–Krylov methods
Structural optimization with mixed-integer programming
Gradient-enhanced neural networks based surrogate method
Ph.D. Aerospace Engineering, University of Michigan, Ann Arbor, 2015 - 2020
MSE. Aerospace Engineering, University of Michigan, Ann Arbor, 2013 - 2015
MS. Applied Math, University of Michigan, Ann Arbor, 2014 - 2015
BSE. Aerospace Engineering, University of Michigan, Ann Arbor, 2012-2013
BSE. Mechanical Engineering, Shanghai Jiao Tong University, 2009-2013