no code implementations • 6 Jun 2022 • Peter Yichen Chen, Jinxu Xiang, Dong Heon Cho, Yue Chang, G A Pershing, Henrique Teles Maia, Maurizio M. Chiaramonte, Kevin Carlberg, Eitan Grinspun
We represent this reduced manifold using continuously differentiable neural fields, which may train on any and all available numerical solutions of the continuous system, even when they are obtained using diverse methods or discretizations.
no code implementations • 15 Sep 2021 • Henrique Teles Maia, Chang Xiao, DIngzeyu Li, Eitan Grinspun, Changxi Zheng
We find that each layer component's evaluation produces an identifiable magnetic signal signature, from which layer topology, width, function type, and sequence order can be inferred using a suitably trained classifier and a joint consistency optimization based on integer programming.
