Are you interested in collaboration? Please do not hesitate emailing Youngsoo!
Youngsoo is a computational math scientist in CASC under Computing directorate at LLNL. His research focuses on developing efficient reduced order models for various physical simulations for time-sensitive decision-making multi-query problems, such as inverse problems, design optimization, and uncertainty quantification. His expertise include various scientific computing disciplines as indicated in "Research interests" below. Together with his collaborators, he has developed various powerful model order reduction techniques, such as machine learning-based nonlinear manifold and space–time reduced order models for nonlinear dynamical systems. He has also developed the component-wise reduced order model optimization algorithm, which enables fast and accurate computational modeling tool for lattice-structure design. He is currently leading data-driven surrogate modeling development team for various physical simulations, with whom he developed the open source codes, libROM and ROM for Laghos, Lagrangian hydrodynamics. He is also involved with quantum computing research. He has earned his undergraduate degree for Civil and Environmental Engineering from Cornell University and his PhD degree for Computational and Mathematical Engineering from Stanford University. He was a postdoc in Sandia National Laboratory and Stanford University prior to joining LLNL in 2017.
"ST-GNAT and SNS: Model order reduction techniques for time-dependent nonlinear system of equations." Applied Mathematics Seminar at UC Berkeley/Lawrence Berkeley Laboratory, California (Sept. 13th 2018).