From the 2 of 10 linked papers with an AI index.
10 papers
Discovering Ordinary Differential Equations with LLM-Based Qualitative and Quantitative Evaluation
Sum Kyun Song, Bong Gyun Shin, Jae Yong Lee
The paper introduces DoLQ, a multi‑agent framework that uses large language models to qualitatively and quantitatively evaluate candidate ordinary differential equations, improving…
Deep Learning-based Surrogate Modelling of the LOD Method for Multiscale Problems
Marc Haltmayer, Jaemin Seo, Yuseung Lee +3
The paper introduces LOD‑MSNO, a hybrid surrogate model that combines the Localized Orthogonal Decomposition (LOD) method with neural operator learning to efficiently solve ellipti…
A residual-based finite element surrogate solver for elliptic partial differential equations
Kyoungjin Jung, Jae Yong Lee, Dongwook Shin
We propose a residual-based finite element surrogate solver for elliptic partial differential equations. The method combines convolutional neural networks with classical finite ele…
Unbiased and Second-Order-Free Training for High-Dimensional PDEs
Jaemin Seo, Surin Lee, Jae Yong Lee
Deep learning methods based on backward stochastic differential equations (BSDEs) have emerged as competitive alternatives to physics-informed neural networks (PINNs) for solving h…
FourierSpecNet: Neural Collision Operator Approximation Inspired by the Fourier Spectral Method for Solving the Boltzmann Equation
Jae Yong Lee, Gwang Jae Jung, Byung Chan Lim +1
The Boltzmann equation, a fundamental model in kinetic theory, describes the evolution of particle distribution functions through a nonlinear, high-dimensional collision operator.…
Conformal mapping based Physics-informed neural networks for designing neutral inclusions
Daehee Cho, Hyeonmin Yun, Jaeyong Lee +1
We address the neutral inclusion problem with imperfect boundary conditions, focusing on designing interface functions for inclusions of arbitrary shapes. Traditional Physics-Infor…