8 papers
Spectrally Safe Neural Operator Warm-Starts for Large-Scale Newton Solvers
Jaemin Oh, Youngkyu Lee, Jerome Darbon +1
Neural operators are increasingly used to warm-start Newton solvers for nonlinear PDEs, on the premise that a low test error places the initial guess inside the basin of attraction…
Perron--Frobenius Operator Matching for Generative Modeling
Shiqi Zhang, Wuwei Wu, Jaemin Oh +2
We introduce Perron--Frobenius Operator Matching (PFOM), a generative framework that matches density evolution via the integral PF operator, subsuming flow, diffusion, and jump mod…
On the Effect of Neural Field Reparameterization for 4DVAR
Jaemin Oh
Four-dimensional variational data assimilation (4DVAR) is a cornerstone of numerical weather prediction, yet it remains computationally intensive and sensitive to initialization du…
Structured State-Space Regularization for Generation-Friendly Image Tokenization
Jinsung Lee, Jaemin Oh, Namhun Kim +3
Image tokenizers play a central role in modern generative models, where the structure of the latent space critically determines the downstream generation performance. A key but und…
Neural Operator-Based Nonlinear Nudging for Chaotic Dynamical Systems
Jaemin Oh, Jinsil Lee, Youngjoon Hong
Nudging is an empirical data assimilation technique that incorporates an observation-driven control term into the model dynamics. The trajectory of the nudged system approaches the…
PIG: Physics-Informed Gaussians as Adaptive Parametric Mesh Representations
Namgyu Kang, Jaemin Oh, Youngjoon Hong +1
The numerical approximation of partial differential equations (PDEs) using neural networks has seen significant advancements through Physics-Informed Neural Networks (PINNs). Despi…