Differentiable Multi-Fidelity Fusion: Efficient Learning of Physics Simulations with Neural Architecture Search and Transfer Learning

no code implementations • 12 Jun 2023 • Yuwen Deng, Wang Kang, Wei W. Xing

With rapid progress in deep learning, neural networks have been widely used in scientific research and engineering applications as surrogate models.

Neural Architecture Search Transfer Learning

Forecasting the outcome of spintronic experiments with Neural Ordinary Differential Equations

1 code implementation • 23 Jul 2021 • Xing Chen, Flavio Abreu Araujo, Mathieu Riou, Jacob Torrejon, Dafiné Ravelosona, Wang Kang, Weisheng Zhao, Julie Grollier, Damien Querlioz

Here we show that a dynamical neural network, trained on a minimal amount of data, can predict the behavior of spintronic devices with high accuracy and an extremely efficient simulation time, compared to the micromagnetic simulations that are usually employed to model them.

Magnetic skyrmion-based synaptic devices

no code implementations • 29 Aug 2016 • Yangqi Huang, Wang Kang, Xichao Zhang, Yan Zhou, Weisheng Zhao

Magnetic skyrmions are promising candidates for next-generation information carriers, owing to their small size, topological stability, and ultralow depinning current density.