11 citations · 20 across the 9 of their papers we have counts for
9 papers
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…
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…
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…
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…
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…
AI-powered Digital Twin of the Ocean: Reliable Uncertainty Quantification for Real-time Wave Height Prediction with Deep Ensemble
Dongeon Lee, Sunwoong Yang, Jae-Won Oh +3
Environmental pollution and fossil fuel depletion have prompted the need for renewable energy-based power generation. However, its stability is often challenged by low energy densi…
Forward and Inverse Simulation of Pseudo-Two-Dimensional Model of Lithium-Ion Batteries Using Neural Networks
Myeong-Su Lee, Jaemin Oh, Dong-Chan Lee +3
In this work, we address the challenges posed by the high nonlinearity of the Butler-Volmer (BV) equation in forward and inverse simulations of the pseudo-two-dimensional (P2D) mod…
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…
Separable Physics-informed Neural Networks for Solving the BGK Model of the Boltzmann Equation
Jaemin Oh, Seung Yeon Cho, Seok-Bae Yun +2
In this study, we introduce a method based on Separable Physics-Informed Neural Networks (SPINNs) for effectively solving the BGK model of the Boltzmann equation. While the mesh-fr…