Jongho Park (박종호, 朴鐘浩)

Research Assistant Professor

Natural Science Research Institute

KAIST, Korea

E-mail: jongho.park (at) kaist.ac.kr

Research Interests

Computational mathematics, Numerical analysis, Domain decomposition methods, Convex optimization, Mathematical imaging, Finite element methods, Neural networks

Publications

Submitted papers

  1. Jongho Park, Jinchao Xu, and Xiaofeng Xu. (2022)

  2. Minwoo Lee and Jongho Park. A numerically efficient output-only system identification framework for stochastically forced self-sustained oscillators (2021)

  3. Chaemin Lee and Jongho Park. Preconditioning for finite element methods with strain smoothing (2021) arXiv

  4. Youngkyu Lee, Jongho Park, and Chang-Ock Lee. Parareal neural networks emulating a parallel-in-time algorithm (2021) arXiv

Published papers

  1. Minwoo Lee and Jongho Park. An optimized dynamic mode decomposition model robust to multiplicative noise. To appear in SIAM Journal on Applied Dynamical Systems. arXiv

  2. Youngkyu Lee, Jongho Park, and Chang-Ock Lee. Two-level group convolution. Neural Networks. 154, 323–332. link arXiv

  3. Jongho Park. Additive Schwarz methods for convex optimization---convergence theory and acceleration. Domain Decomposition Methods in Science and Engineering XXVI, 673680, Lecture Notes in Computational Science and Engineering, 145, Springer, 2022. link

  4. Chang-Ock Lee, Youngkyu Lee, and Jongho Park. A parareal architecture for very deep neural network. Domain Decomposition Methods in Science and Engineering XXVI, 385393, Lecture Notes in Computational Science and Engineering, 145, Springer, 2022. link

  5. Jongho Park. Fast gradient methods for uniformly convex and weakly smooth problems. Advances in Computational Mathematics, 48, Paper No. 34 (2022) link arXiv

  6. Chaemin Lee, Minam Moon, and Jongho Park. A gradient smoothing method and its multiscale variant for flows in heterogeneous porous media. Computer Methods in Applied Mechanics and Engineering, 395, Paper No. 115039 (2022) link

  7. Jongho Park. Additive Schwarz methods for convex optimization with backtracking. Computers & Mathematics with Applications, 113(1), 332344 (2022) link arXiv

  8. Jongho Park. Accelerated additive Schwarz methods for convex optimization with adaptive restart. Journal of Scientific Computing, 89, Paper No. 58 (2021) link arXiv

  9. Chang-Ock Lee and Jongho Park. A dual-primal finite element tearing and interconnecting method for nonlinear variational inequalities utilizing linear local problems. International Journal for Numerical Methods in Engineering, 122(22), 6455–6475 (2021) link

  10. Chaemin Lee and Jongho Park. A variational framework for the strain-smoothed element method. Computers & Mathematics with Applications, 94, 7693 (2021) link arXiv

  11. Chang-Ock Lee, Eun-Hee Park, and Jongho Park. Corrigendum to "A dual iterative substructuring method with a small penalty parameter", [J. Korean Math. Soc. 54 (2017), No. 2, 461477]. Journal of the Korean Mathematical Society, 58(3), 791797 (2021) link arXiv

  12. Jongho Park. Pseudo-linear convergence of an additive Schwarz method for dual total variation minimization. Electronic Transactions on Numerical Analysis, 54, 176197 (2021) link arXiv

  13. Chang-Ock Lee and Jongho Park. Recent advances in domain decomposition methods for total variation minimization. Journal of the Korean Society for Industrial and Applied Mathematics, 24(2), 161197 (2020) link

  14. Jongho Park. An overlapping domain decomposition framework without dual formulation for variational imaging problems. Advances in Computational Mathematics, 46(4), Paper No. 57 (2020) link arXiv

  15. Jongho Park. Additive Schwarz methods for convex optimization as gradient methods. SIAM Journal on Numerical Analysis, 58(3), 14951530 (2020) link arXiv

  16. Chang-Ock Lee and Jongho Park. Fast nonoverlapping block Jacobi method for the dual RudinOsherFatemi model. SIAM Journal on Imaging Sciences, 12(4), 20092034 (2019) link arXiv

  17. Chang-Ock Lee and Jongho Park. A finite element nonoverlapping domain decomposition method with Lagrange multipliers for the dual total variation minimizations. Journal of Scientific Computing, 81(3), 23312355 (2019) link arXiv

  18. Chang-Ock Lee, Eun-Hee Park, and Jongho Park. A finite element approach for the dual RudinOsherFatemi model and its nonoverlapping domain decomposition methods. SIAM Journal on Scientific Computing, 41(2), B205B228 (2019) link arXiv

  19. Chang-Ock Lee, Changmin Nam, and Jongho Park. Domain decomposition methods using dual conversion for the total variation minimization with $L^1$ fidelity term. Journal of Scientific Computing, 78(2), 951970 (2019) link

Slides

  1. Neural networks with parallel structures: exploiting mathematical ideas in designing neural networks. Numerical Methods for Data Science and Engineering Seminar, Texas State University, March 25, 2022. slide

  2. Additive Schwarz methods for convex optimization—convergence theory and acceleration. CCMA Seminar on Mathematics of Data and Computation, Pennsylvania State University, January 20, 2022. slide

  3. Mathematical theory of finite element methods with strain smoothing. Seminar in Department of Applied Mathematics, Kyung Hee University, January 17, 2022. slide

  4. An optimized dynamic mode decomposition model robust to multiplicative noise. Seminar in Department of Mathematical Sciences, KAIST, September 13, 2021. slide

  5. Fast gradient methods for uniformly convex and weakly smooth problems. Seminar in Department of Mathematical Sciences, KAIST, April 5, 2021. slide

  6. Domain decomposition methods for convex optimization in image processing: focusing on total variation minimization. Ph.D. Defense, Department of Mathematical Sciences, KAIST, May 31, 2019. slide