April 2, 2024, 7:42 p.m. | Aarush Sinha

cs.LG updates on arXiv.org arxiv.org

arXiv:2404.00618v1 Announce Type: new
Abstract: In this study, we propose a multi branched network approach to predict the dynamics of a physics attractor characterized by intricate and chaotic behavior. We introduce a unique neural network architecture comprised of Radial Basis Function (RBF) layers combined with an attention mechanism designed to effectively capture nonlinear inter-dependencies inherent in the attractor's temporal evolution. Our results demonstrate successful prediction of the attractor's trajectory across 100 predictions made using a real-world dataset of 36,700 time-series …

abstract architecture arxiv attention behavior cs.cv cs.lg cs.ne dynamics function network network architecture neural network physics study type

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