Mechanical Engineering undergraduate interested in physics-informed and data-driven modeling for mechanical systems.
I use GitHub to organize undergraduate projects and self-study work before graduate research. My current focus is on applying machine learning, simulation, and physical modeling to mechanical system analysis, diagnosis, and control.
- Physics-Informed Machine Learning
- Mechanical System Modeling and Vibration
- Data-driven Fault Diagnosis and PHM
- Simulation-based Analysis and Control
| Project | Role in My Research Direction | Description |
|---|---|---|
| PINN | Physics-informed modeling | Modified and studied a PINN example for a damped mass-spring system from a mechanical engineering perspective. |
| NASA-Airfoil-Self-Noise | Noise and signal/data analysis | Exploratory analysis and regression modeling on NASA airfoil self-noise data. |
| manufacturing-quality-dnn | Process data and fault classification | DNN-based quality classification on KAMP precision machining process data. |
| mechanical-design-projects | Physics-based simulation | Undergraduate mechanical design projects using ANSYS, MATLAB, FEA, and design optimization. |
| 2025-CARSA | Simulation-based control study | Steering-control study using MATLAB/Simulink and IPG CarMaker, with simulation-based training data expansion. |
| public-safety-first-response | Safety-aware system prototype | Prototype workflow linking risk recognition with safety-aware drone response. |
I am especially interested in combining physical laws, simulation data, and sensor/process data for mechanical system modeling, fault diagnosis, and control.