1 citations · 1 across the 3 of their papers we have counts for
5 papers · 1 filter
PDEfuncta: Spectrally-Aware Neural Representation for PDE Solution Modeling
Minju Jo, Woojin Cho, Uvini Balasuriya Mudiyanselage +3
Scientific machine learning often involves representing complex solution fields that exhibit high-frequency features such as sharp transitions, fine-scale oscillations, and localiz…
SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields
David Keetae Park, Xihaier Luo, Guang Zhao +3
Spatiotemporal learning is challenging due to the intricate interplay between spatial and temporal dependencies, the high dimensionality of the data, and scalability constraints. T…
Generalizable Implicit Neural Representations via Parameterized Latent Dynamics for Baroclinic Ocean Forecasting
Guang Zhao, Xihaier Luo, Seungjun Lee +7
Mesoscale ocean dynamics play a critical role in climate systems, governing heat transport, hurricane genesis, and drought patterns. However, simulating these processes at high res…
Inducing Point Operator Transformer: A Flexible and Scalable Architecture for Solving PDEs
Seungjun Lee, Taeil Oh
Solving partial differential equations (PDEs) by learning the solution operators has emerged as an attractive alternative to traditional numerical methods. However, implementing su…
Identifying Physical Law of Hamiltonian Systems via Meta-Learning
Seungjun Lee, Haesang Yang, Woojae Seong
Hamiltonian mechanics is an effective tool to represent many physical processes with concise yet well-generalized mathematical expressions. A well-modeled Hamiltonian makes it easy…