Yoonsang Lee email : ylee263 at lbl.gov

Yoonsang Lee

I am a postdoctoral fellow at Center for Computational Sciences and Engineering at Lawrence Berkeley National Labratory, working on applied mathematics and computational issues in prediction and uncertainty quantification of complex dynamical systems. I am interested in particular in computational methods to efficiently combine numerical prediction models with data, which are scalable for big data and high-dimensional systems.

Before joining LBL, I was a postdoctoral fellow at Courant Institute, New York University woking with Professor Andrew J. Majda. I received my PhD in Mathematics under the supervision of Professor Bjorn Engquist at the University of Texas at Austin.

Research Interests

"The difficult is what takes a little time; the impossible is what takes a little longer." - Fridtjof Hansen, Nobel Peace Prize Laureate, 1922

News

Education

My CV is available for download here [link] (updated : Sep 11, 2017)
"The purpose of computing is insight, not numbers." - Richard Hamming

My research focuses on mathematical problems in prediction and uncertainty quantification of complex dynamical systems. In particular I am intrested in robust and efficient computational methods to combine numerical prediction models with data, which are scalable for big data and high-dimensional systems.

The mathematical framework of my research shares with research areas known as

Application areas of my research include but not limited to geophysical fluid systems and combustion models. You can find more details from my publications.

Work in progress

Journal papers and preprints

Books

Teaching

I find fun in teaching mathematics, physics and computer science, especially their interdisciplinary applications in science and engineering. Applyting theories to applications is one way to learn mathematics and science but I believe that it is more efficient to generalize ideas from examples (or experiences) and then apply to other applications. Thus motivating students from real-world examples is the primary goal of my teaching, which is followed by generalization of ideas and applications to other examples. Here are some comments from my students

Comments from my students

PDE solvers

It is a good exercise to develop your own PDE solvers based on what you have learned in Numerical Analysis. However, for practical research computations, it is strongly recommended to use PDE solvers developed and refined by many researchers. There are many PDE solvers freely available online. Among others, I recommend the following PDE solvers - AMReX @ LBL and PETSc @ Argonne - for robust and efficient computations. Check out the following links.

Data Assimilation Codes

Data assimilation combines a numerical forecast model with observational data to improve the prediction skill. If you are interested in testing/running data assimilation, please check the following programs.

If you are interested in a MATLAB program for data assimilation, you can check the following book by Law, Stuart and Zygalakis, which contains MATLAB codes