Profile

• Name: Jeahan Jung
• Email: obok13 at postech dot ac dot kr

Research Interests and Technical Skills

• Scientific machine learning
• Uncertainty quantification
• Bayesian deep learning
• physics-informed neural network (PINN)
• Polynomial chaos
• Partial differential equation (PDE)
• Python, JAX, Tensorflow, Pytorch, Matlab

Education

• Pohang University of Science and Technology (Pohang, South Korea)
  • Integrated Ph.D Program of Mathematics (2017 - 2023)
  • Bachelor of Mathematics (2013 - 2017)
• Gwangju Science High School (Gwangju, South Korea)
  • Early graduation (2011 - 2013)

Publications

• Submitted
  • Jeahan Jung, Heechang Kim, Hyomin Shin, and Minseok Choi. “CEENs: Causality-enforced evolutional networks for solving time-dependent partial differential equation problems”. 2023
  • Jeahan Jung, Hyomin Shin, and Minseok Choi, “Bayesian deep learning framework for uncertainty quantification in stochastic partial differential equations”, 2023
• Published
  • Jeahan Jung and Minseok Choi. "Data-driven Method to Quantify Correlated Uncertainties." IEEE Access (2023).
  • 포디솔루션컨소시움. "이상기후 정보 서비스를 위한 가이던스 및 콘텐츠 개발". 기상청, 2019
  • Jeahan Jung and Jeong-Yoo Kim, "Cheap Talk by Two Senders in the Presence of Network Externalities", Korean Economic Review 35 (2019): 249-274.

Conference papers

• Minseok Choi and Jeahan Jung. “Bayesian deep learning for uncertainty quantification”. International Conference on Spectral and High Order Methods, 2023-08-18
• Minseok Choi and Jeahan Jung. “Bayesian deep learning framework for uncertainty quantification in stochastic systems”. 2023년도 대한수학회 봄 연구발표회. 2023-04-28.