Research & projects
Four problems I kept coming back to.
A mix of fusion physics, fluid dynamics, space science, and the compute needed to make them practical.
Tokyo · PhD research
Physics-based ML Scheme for Accelerating 2D Lower-Hybrid Wave Simulation on TST-2
Physics-Informed Neural Operator surrogate for lower hybrid waves — 281.8× faster inference in rectangular domains, 25.8× in TST-2 circular geometry, within a 498 MB footprint.
Read the project →
Moldex3D · Industry R&D
Pretrained Fourier Neural Operator for Non-Newtonian Fluid Dynamics
FNO surrogate for mold-flow analysis in Moldex3D — predicting pressure (MAE < 3 Pa) and melt front time (MAE < 0.3 s).
Read the project →
Taiwan/UK · Published research
Reg-PINNs for Magnetopause Tracking
Regression-based Physics-Informed Neural Networks combining empirical models with deep learning — ~30% RMSE reduction vs. Shue et al. [1998].
Read the project →
London · High-performance computing
Parallel Computing, with Applications in Quantitative Strategies
Multicore benchmarking of a volatility-momentum equity strategy on an HPC cluster.
Read the project →