Sunwoong Yang

Postdoc @ KAIST in South Korea

Research Topic: Physics-Guided AI for Aerodynamic Simulation & Design

▼ Key components of Physics-Guided AI

1️⃣ Data Efficiency and Practicality

Trustworthiness requires special data handling strategies (e.g., multi-fidelity modeling) for reliable performance despite the limitations of sparse/imperfect practical datasets

2️⃣ Physical Consistency

Trustworthiness requires that models respect established physical laws, ensuring predictions are realistic and preventing non-physical outcomes

3️⃣ Robustness in Spatio-Temporal Prediction

For dynamic systems, trustworthiness requires that predictions remain stable and accurate over long time horizons, effectively mitigating error accumulation

4️⃣ Reliable Uncertainty Quantification (UQ)

Trustworthiness depends on a model accurately quantifying its own uncertainty, enabling engineers to make informed, risk-aware decisions

▼ Specialization of Physics-Guided AI for Aerodynamic Simulation & Design

My research systematically integrates above four key components into a unified end-to-end framework,
providing a roadmap as below for developing physics-guided AI in aerodynamic simulation & design

Experience

Postdoc

KAIST (Sep. 2023 ~ Present)

Mechanical Engineering Research Institute

Supervisor: Prof. Namwoo Kang
Funding: Sejong Fellowship (NRF, 24.05~29.04)

Education

Ph.D.

Seoul National University (Mar. 2020 ~ Aug. 2023)

Department of Aerospace Engineering

Supervisor: Prof. Kwanjung Yee

Thesis: Efficient Aerodynamic Design via Data-driven Approaches
(Outstanding Doctoral Dissertation Award in SNU)

M.S.

Seoul National University (Mar. 2018 ~ Feb. 2020)

Department of Mechanical and Aerospace Engineering

Supervisor: Prof. Kwanjung Yee

Thesis: Planform Optimization of UCAV Considering Longitudinal Stability and Low-observability Using Variable-fidelity

B.S.

Seoul National University (Mar. 2014 ~ Feb. 2018)

Department of Mechanical and Aerospace Engineering

Funded Research Projects

Title: Development of AI-based Flow Prediction Framework Considering Versatility and User-Friendliness in Digital Twins
Sponsor: National Research Foundation of Korea (Sejong Fellowship)
Period: 2024.05.01 ~ 2029.04.30 (5 years, 500,000,000 KRW)
Role: PI
Title: Development of AI algorithms for preform design for blow molding
Sponsor: Korea Institute of Industrial Technology (KITECH)
Period: 2024.05.08 ~ 2024.08.31 (4 months, 20,000,000 KRW)
Role: PI

Hope you enjoy exploring!