Invited Talks
Physics-informed machine learning for inverse problems
2024.05.31, Invited Talk, CM2LA workshop in machine learning and numerical analysis
Physics-informed machine learning for inverse problems
2024.05.18, Invited Talk, KSIAM spring meeting
A PINN approach for identifying governing parameters of noisy thermoacoustic systems
2024.04.20, Special Session, KMS spring meeting
Sobolev Training for Neural Networks and Its Applications
2024.01.31, Special Conference on Physics Informed Machine Learning, The Korean Society of Mechanical Engineering
Physics Informed Neural Networks: Convergence and Applications
2023.10, Invited Seminar, Ulsan National Institute of Science and Technology
Physics Informed Neural Networks for inverse problems
2023.10, Special Session, KMS special conference with 2022 Fields Medalists
Physics Informed Neural Networks: Convergence and Applications
2023.08, Invited Seminar, National Institute for Mathematical Sciences.
Physics Informed Neural Networks: Convergence and Applications (youtube link)
2023.08, Special Conference on Physics Informed Machine Learning, The Korean Society of Mechanical Engineering
Physics Informed Neural Networks and Its Applications
2023.03, Invited Seminar, National Institute for Mathematical Sciences.
Using Ansatzes to Find Approximate Solutions of PDEs via Neural Networks.
2022.08, KSIAM-MINDS-NIMS International Conference on Machine Learning and PDEs.
Physics Informed Neural Networks.
2022.08, Invited Seminar, KAIST.
Tutorial on Machine Learning and Numerical PDEs.
2022.06, AMSquare Corporation.
Tutorial on Machine Learning and Numerical PDEs.
2022.06, KSIAM Spring Meeting.
AL-PINNs: Augmented Lagrangian relaxation method for PINNs.
2022.04, KMS Spring Meeting.
Recent advances in Physics Informed Neural Networks.
2022.04, Invited Seminar, Sungkyunkwan University.
Recent advances in Physics Informed Neural Networks.
2021.12, Invited Seminar, Kyung Hee University.
Constrained deep learning for PDEs.
2021.11, MINDS Workshop on Recent Progress in Data Science and Applications, POSTECH.
Recent Advances in neural networks methods for solving PDEs.
2021.08, SAARC Workshop on Mathematics and Machine Learning, KAIST.
Recent Advances in neural networks methods for solving PDEs.
2021.06, Analysis Seminar, KIAS.
Traveling wave solutions of partial differential equations via neural networks.
2021.05, Colloquium Talk, Hanyang University.
Sobolev Training for the neural network solutions of PDEs.
2021.04, SAARC Monthly Meeting, KAIST,
Sobolev Training for the neural network solutions of PDEs.
2020.10, KMS Spring Meeting, KMS,
Forward-Inverse problems and their applications to COVID-19 spread model.
2020.10, ReaDiNet 2020: An online conference on mathematical biology,