4 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 to the Training Error and Generalization Gap of Random Feature Models
Taeyoung Kim
We investigate random feature models in which neural networks sampled from a prescribed initialization ensemble are frozen and used as random features, with only the readout weight…
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…