Research

work in process

• Model-based RL via linearized HJ with  Yeoneung Kim

• Barron norm estimates for elliptic/parabolic equations

• Cho, N. K. Policy optimization in Reinforcement Learning for column generation (work in process) 

• Namkyeong Cho, Lp resolvent estimates for the Stokes operator on non-smooth domains with conormal derivative boundary conditions (work in process) 

• Physics-Informed Machine Learning

• Selective-Supervised Mutual Learning 

submitted/preprints

• Namkyeong Cho, Junseung Ryu, Youngwook Bin, Hyung Ju Hwang,  Reconstruction-based Wafer Anomaly Detection in Semiconductor Manufacturing with Point Transformers (submitted)

• Namkyeong Cho, Yeoneung Kim, On the stability of Lipschitz continuous control problems and its application to reinforcement learning (submitted)  Arxiv

• Yeongjong Kim, Yeoneung Kim, Minseok Kim, Namkyeong Cho, Neural Policy Iteration for Stochastic Optimal Control: A Physics-Informed Approach (submitted)

• Namkyeong Cho*, SungWoong Cho*, Youngjoon Hong*, Hyung Ju Hwang*, Jae Yong Lee*, Hwijae Son*, Neural advection-diffusion equation for long-term climate dynamics (submitted, * equal contribution)

published papers

1. Yeongjong Kim*, Namkyeong Cho*, Minseok Kim, Yeoneung Kim, Physics-informed approach for exploratory Hamilton--Jacobi--Bellman equations via policy iterations, with  (2026, AAAI, * equal contribution, Arxiv)

2. Representation of the solution of fractional  Schrödinger  equations with a shallow neural network, with Junseung Ryu, Hyung Ju Hwang (JDE, 2026, corresponding author, journal)

3. Namkyeong Cho*, Junseung Ryu*, Hyung Ju Hwang,  MBNO: Mamba-Based Neural Operators for Solving Partial Differential Equations (JCP, 2026, * equal contribution, journal)

4. Namkyeong Cho*, Junseung Ryu*, Hyung Ju Hwang. Sobolev Training for Operator Learning (JCP, 2025, * equal contribution, Arxiv, ,journal)

5. Junseung Ryu*, Namkyeong Cho*, Hyung Ju Hwang,  A Neural Operator Unifying Graph Neural Networks and Point-Transformer (IEEE ACCESS, 2025, * equal contribution, journal)

6. Jaesun Shin, EUNJOO JEON, Taewon Cho, Namkyeong Cho, and Youngjune Gwon. Line Graph Vietoris-Rips Persistence Diagram for Topological Graph Representation Learning (Journal of Machine Learning Research, 2024,  journal) 

7. Namkyeong Cho, Taewon Cho, Jaesun Shin, EUNJOO JEON, Taehee Lee. Universal Embedding for Pre-trained Models and Data Bench (Neurocomputing, 2025, journal) 

8. Cho, N. K. Global gradient estimates for shear thinning-type Stokes system on the non-smooth domains  (to appear in EJDE) 

9. Cho, N. K. Second order regularity for the p(x)-Laplace equations with L2 data on the right-hand side (Nonlinear Analysis, 2023)  journal

10. Byun. S. S., & Cho, N. K. Higher differentiability for solutions of a general class of nonlinear elliptic obstacle problems with Orlicz growth (NoDea, 2022) ,  journal

11. Cho, N. K. Global regularity of shear thickening Stokes system with Dirichlet boundary condition (Journal of Mathematical Fluid Mechanics, 2022) ,  journal

12. Byun, S. S., Cho, N. K, & Youn, Y. H. Existence and regularity result for measure data problems with general growth. (Calculus of Variation, 2021),  journal

13.  Byun, S. S., Cho, N. K, & Youn, Y. H. Calder ́on-Zygmund estimates for a class of double phase problems with measure data (Journal of Mathematical Analysis and Applications, 2021),  journal

14. Byun, S. S., Cho, N. K. & Lee, H. S Maximal differentiability for a general class of quasilinear elliptic equations with right-hand side measures (International Mathematics Research Notices, 2021) ,  journal

15. Byun, S. S., Cho, N. K. & Song, K Optimal fractional differentiability for non-linear parabolic measure data problems (Applied Mathematics Letters, 2021),  journal