Mathematical Theory and Learning Algorithms of Structure-Preserving Neural Operators for Partial Differential Equations (편미분방정식을 위한 구조보존 Neural Operator의 수학적 이론 및 학습 알고리즘)

(Outstanding Young Researcher Program (Type B), Funded by the National Research Foundation (NRF) of Korea, Mar 2026 - Feb 2031)

• Developing theoretical foundations and learning algorithms for structure-preserving neural operators, and establishing a unified framework that integrates design principles, learning algorithms, uncertainty quantification (UQ), and inverse problem applications for PDEs.

Hybrid PINNs-Based Structural Analysis Prediction Technology Development

(Hyundai Motor Company, Mar 2026 - Sep 2026)

• Developing a hybrid physics-informed AI framework that integrates pre-trained simulation models with PINNs to enhance structural analysis accuracy and improve prediction reliability in real-world field conditions.

Global Basic Research Laboratory (BRL) (글로벌 기초연구실) program

(Funded by the National Research Foundation (NRF) of Korea, June 2025 - May 2028)

• Analysis-Guided Neural Operator for Inverse Problems 

(역문제 해결을 위한 해석적 방법 기반 Neural Operator 연구)

Development of Physics-Informed AI Technology for Rapid Large-Area Thermal Analysis

(SAMSUNG, April 2025 - Mar 2026)

Enhancing CFD Analysis Accuracy and Optimizing Film Cooling Hole Geometry

(Doosan, July 2024 - June 2025)

• Developing AI Algorithms to Accelerate CFD and Enhance Accuracy

Apply an unsupervised learning-based AI algorithm to the sensor data of the chamber

(SAMSUNG, Sep 2018 - Aug 2019)

• Methodology Research

• Develop a health index

• Real-time monitoring of facilities applicable to the field

Developing a Methodology for Anomaly Detection on the Casting Line

(POSCO, Sep 2017 - June 2018)

• Methodology Research

• Roll Gap Analysis on the Casting Line Data