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Our group focuses on scientific machine learning, which combines the traditional computational mechanics and machine learning methods, for solving time-dependent partial differential equations arising from various scientific and engineering problems. We leverage cutting-edge techniques in scientific machine learning (SciML) to address time-dependent partial differential equations in complex scientific and engineering problems.

I obtained my Bachelor of Engineering in Computer Science and Electrical Engineering, followed by a Master of Engineering in Spatial Design and Engineering from Handong Global University, in 2007 and 2009 respectively. Then, I pursued and completed my Ph.D. in Engineering Mechanics at the University of Texas at Austin in 2019. After that, I dedicated about four years to working at Argonne National Laboratory. In 2024, I started a new position as an assistant professor in the Department of Computer Convergence Software at Korea University Sejong Campus. Details can be found in my CV.

Recent news:

• Feb. 2024: Learning Subgrid-scale Models in Discontinuous Galerkin methods with Neural Ordinary Differential Equations is under review.

• Oct. 2023: Multirate Partitioned Runge-Kutta Methods for Coupled Navier-Stokes Equations was published.

• Jun. 2023: Learning Subgrid-scale Models with Neural Ordinary Differential Equations was published.