Youngkyu Lee (이영규)

I am a postdoctoral researcher in Applied Mathematics at Brown University (CRUNCH Group led by Prof. George Karniadakis). I received a Ph.D. degree in Mathematcial Sciences in 2023 (Advisor: Chang-Ock Lee) from KAIST.

My research interests include computational mathematics, parallel computation, and scientific machine learning. I am particularly interested in developing the preconditioners to accelerate the neural or numerical solvers for scientific problems.

Curriculum Vitae, Google Scholar

Education

• Ph. D. Mathematical Sciences, August 2023, KAIST, South Korea
  Advisor: Chang-Ock Lee

• B.S. Mathematics, February 2017, Kyung Hee University, South Korea

Publication

Submitted papers

1. Alena Kopaničáková, Youngkyu Lee, and George Em Karniadakis. Leveraging Operator Learning to Accelerate Convergence of the Preconditioned Conjugate Gradient Method (2025). arXiv

2. Youngkyu Lee, Shanqing Liu, Jerome Darbon, and George Em Karniadakis. Automatic discovery of optimal meta-solvers for time-dependent nonlinear PDEs (2025). arXiv

3. Youngkyu Lee, Shanqing Liu, Jerome Darbon, and George Em Karniadakis. Automatic discovery of optimal meta-solvers via multi-objective optimization (2024). arXiv

4. Youngkyu Lee, Alena Kopaničáková, and George Em Karniadakis. Two-level Overlapping Additive Schwarz Preconditioners for Training Scientific Machine Learning Applications (2024). arXiv

Published/Accepted papers

1. Youngkyu Lee, Jongho Park, and Chang-Ock Lee. Balanced group convolution: An improved group convolution based on approximability estimates. Pattern Anal Applic 28, 161 (2025). link arXiv

2. Sunwoong Yang, Youngkyu Lee, and Namwoo Kang. Data-efficient deep operator network for unsteady flow: A multi-fidelity approach with physics-guided subsampling. Computer Methods in Applied Mechanics and Engineering, 446, 118254 (2025). link arXiv

3. Youngkyu Lee, Shanqing Liu, Zongren Zou, Adar Kahana, Eli Turkel, Rishikesh Ranade, Jay Pathak, and George Em Karniadakis. Fast meta-solvers for 3D complex-shape scatterers using neural operators trained on a non-scattering problem. Computer Methods in Applied Mechanics and Engineering, 446, 118231 (2025). link arXiv

4. Chang-Ock Lee, Youngkyu Lee, and Byungeun Ryoo. A Nonoverlapping Domain Decomposition Method for Extreme Learning Machines: Elliptic Problems. Computers and Mathematics with Applications, 189, 109–128 (2025). link arXiv

5. Youngkyu Lee,  Jongho Park, and Chang-Ock Lee. Parareal neural networks emulating a parallel-in-time algorithm. IEEE Transactions on Neural Networks and Learning Systemsm, 35, 6353–6364 (2024). link arXiv

6. Chang-Ock Lee, Youngkyu Lee, and Jongho Park. A parareal architecture for very deep neural network. Domain Decomposition Methods in Science and Engineering XXVI, 407–415, Lecture Notes in Computational Science and Engineering, 145, Springer, 2023. link

7. Youngkyu Lee,  Jongho Park, and Chang-Ock Lee. Two-level group convolution. Neural Networks, 154, 323–332 (2022). link arXiv

Teaching Experiences

1. Teaching Assistant, Introduction to Scientific Machine Learning (Fall 2022), KAIST
   - Graded homework biweekly. Conducted office hour (Q&A). Graded midterm and final exam.

2. Teaching Assistant, Linear Algebra for Data Science (Spring 2022), KAIST
   - Graded quiz problems biweekly. Lectured in the recitation class weekly. Graded midterm and final exam.

3. Teaching Assistant, Calculus II (Spring 2021), KAIST
   - Graded homework weekly. Graded midterm.

4. Teaching Assistant, Cakculus I (Spring 2020), KAIST
   - Graded homework weekly. Graded midterm.

5. Teaching Assistant, (Spring 2019), KAIST
   Introduction to Numerical Analysis
   - Graded homework biweekly. Graded midterm and final exams.
   Introduction to Linear Algebra
   - Prepared and graded quiz problems weekly. Lectured in the recitation class weekly. Graded midterm.

6. Teaching Assistant, (Fall 2018), KAIST
   Introduction to Differential Geometry
   - Lectured in the recitation class biweekly. Graded midterm and final exam.
   Calculus II (Fall 2018), KAIST
   - Graded homework weekly. Graded final exam.

7. Teaching Assistant, Introduction to Linear Algebra (Spring 2018), KAIST
   - Prepared and graded quiz problems weekly. Lectured in the recitation class weekly. Graded midterm.