5 papers
Diffusion-Based Refinement for Kilometer-Scale Probabilistic Precipitation Nowcasting
Dohyun Park, Changhoon Song, Tengyuan Chang +2
Localized extreme precipitation is a major trigger of urban flash floods and landslides, yet producing nowcasts that combine fine spatial detail with probabilistic uncertainty rema…
Extreme Weather Nowcasting via Local Precipitation Pattern Prediction
Changhoon Song, Teng Yuan Chang, Youngjoon Hong
Accurate forecasting of extreme weather events such as heavy rainfall or storms is critical for risk management and disaster mitigation. Although high-resolution radar observations…
A data free neural operator enabling fast inference of 2D and 3D Navier Stokes equations
Junho Choi, Teng-Yuan Chang, Namjung Kim +1
Ensemble simulations of high-dimensional flow models (e.g., Navier Stokes type PDEs) are computationally prohibitive for real time applications. Neural operators enable fast infere…
Singular layer PINN methods for Burgers' equation
Teng-Yuan Chang, Gung-Min Gie, Youngjoon Hong +1
In this article, we present a new learning method called sl-PINN to tackle the one-dimensional viscous Burgers problem at a small viscosity, which results in a singular interior la…
Singular layer Physics Informed Neural Network method for Plane Parallel Flows
Teng-Yuan Chang, Gung-Min Gie, Youngjoon Hong +1
We construct in this article the semi-analytic Physics Informed Neural Networks (PINNs), called {\em singular layer PINNs} (or {\em sl-PINNs}), that are suitable to predict the sti…