5 papers
A data free neural operator enabling fast inference of 2D and 3D Navier Stokes equations
Junho Choi, Teng-Yuan Chang, Namjung Kim +1
Ensemble simulations of high-dimensional flow models (e.g., Navier Stokes type PDEs) are computationally prohibitive for real time applications. Neural operators enable fast infere…
Toward a Robust and Generalizable Metamaterial Foundation Model
Namjung Kim, Dongseok Lee, Jongbin Yu +4
Advances in material functionalities drive innovations across various fields, where metamaterials-defined by structure rather than composition-are leading the way. Despite the rise…
MaD-Scientist: AI-based Scientist solving Convection-Diffusion-Reaction Equations Using Massive PINN-Based Prior Data
Mingu Kang, Dongseok Lee, Woojin Cho +5
Large language models (LLMs), like ChatGPT, have shown that even trained with noisy prior data, they can generalize effectively to new tasks through in-context learning (ICL) and p…
Simple arithmetic operation in latent space can generate a novel three dimensional graph metamaterials
Namjung Kim, Dongseok Lee, Chanyoung Kim +2
Recent advancements in artificial intelligence (AI)-based design strategies for metamaterials have revolutionized the creation of customizable architectures spanning nano- to macro…
Spectral operator learning for parametric PDEs without data reliance
Junho Choi, Taehyun Yun, Namjung Kim +1
In this paper, we introduce the Spectral Coefficient Learning via Operator Network (SCLON), a novel operator learning-based approach for solving parametric partial differential equ…