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cs.LG2025
Can Data-Driven Dynamics Reveal Hidden Physics? There Is A Need for Interpretable Neural Operators
Wenhan Gao, Jian Luo, Fang Wan +4
Recently, neural operators have emerged as powerful tools for learning mappings between function spaces, enabling data-driven simulations of complex dynamics. Despite their success…
cs.LG2025
Kolmogorov-Arnold Representation for Symplectic Learning: Advancing Hamiltonian Neural Networks
Zongyu Wu, Ruichen Xu, Luoyao Chen +3
We propose a Kolmogorov-Arnold Representation-based Hamiltonian Neural Network (KAR-HNN) that replaces the Multilayer Perceptrons (MLPs) with univariate transformations. While Hami…
cs.LG2025★ 1 cited
An Iterative Framework for Generative Backmapping of Coarse Grained Proteins
Georgios Kementzidis, Erin Wong, John Nicholson +2
The techniques of data-driven backmapping from coarse-grained (CG) to fine-grained (FG) representation often struggle with accuracy, unstable training, and physical realism, especi…