We analyze scientific phenomena by combining mathematical modeling with deep learning, developing scientific machine learning models applicable across dynamical systems, industrial processes, medicine, and the life sciences.

Citations

475

h-index

11

i10

11

Per Google Scholar · last updated 2026년 9월 24일 06:01 · Scholar profile

Research areas

• Physics-Informed Neural Networks (PINNs)
• Parameter estimation for differential equations
• Density-PINN
• Inference of dynamical systems via machine learning
• Clinical data analysis and medical AI
• Learning from imperfect data
• Symbolic regression

Recent publications

• 2026 Predictive Modeling of Disinfectant Efficacy: Integrating Bacterial Inactivation Kinetics with Human Health Risk Assessment Food Research International · Cited 0
• 2026 A Machine Learning-based Screening Tool for classifying Post-traumatic Stress Disorder in Firefighters BMC Psychiatry · Cited 0
• 2026 Estimation of System Parameters Including Repeated Cross-Sectional Data: Emulator-Informed Deep-Generative Model SIAM Journal on Scientific Computing · Cited 3

All publications →

News

• 2024Selected as KUS New Challenger
• 2024IBS Outstanding Researcher Award

Joining the lab

We welcome undergraduate and graduate students. Please get in touch. ajouhtj@ajou.ac.kr