1 citations · 1 across the 6 of their papers we have counts for
6 papers
A Robust In-Context Model for Conservation Laws: Injecting Context into Flux Neural Operators via Recurrent Vision Transformers
Taeyoung Kim, Joon-Hyuk Ko
We propose an architecture that augments the Flux Neural Operator (Flux NO), which combines the classical finite volume method (FVM) with neural operators, with ViT-based context i…
Loop Corrections in Random Feature Models: Training Error and Generalization Gap
Taeyoung Kim
We study fixed-design random feature ridge regression beyond the mean-kernel approximation. The expectation is taken over the frozen-feature ensemble, conditional on the training s…
Why Rectified Power Unit Networks Fail and How to Improve It: An Effective Field Theory Perspective
Taeyoung Kim, Myungjoo Kang
The Rectified Power Unit (RePU) activation function, a differentiable generalization of the Rectified Linear Unit (ReLU), has shown promise in constructing neural networks due to i…
Neural Operators Learn the Local Physics of Magnetohydrodynamics
Taeyoung Kim, Youngsoo Ha, Myungjoo Kang
Magnetohydrodynamics (MHD) plays a pivotal role in describing the dynamics of plasma and conductive fluids, essential for understanding phenomena such as the structure and evolutio…
Approximating Numerical Fluxes Using Fourier Neural Operators for Hyperbolic Conservation Laws
Taeyoung Kim, Myungjoo Kang
Traditionally, classical numerical schemes have been employed to solve partial differential equations (PDEs) using computational methods. Recently, neural network-based methods hav…
Bounding the Rademacher Complexity of Fourier neural operators
Taeyoung Kim, Myungjoo Kang
A Fourier neural operator (FNO) is one of the physics-inspired machine learning methods. In particular, it is a neural operator. In recent times, several types of neural operators…