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physics.comp-ph2019
AI Feynman: a Physics-Inspired Method for Symbolic Regression
Silviu-Marian Udrescu, Max Tegmark
A core challenge for both physics and artificial intellicence (AI) is symbolic regression: finding a symbolic expression that matches data from an unknown function. Although this p…
physics.comp-ph2018
Toward an AI Physicist for Unsupervised Learning
Tailin Wu, Max Tegmark
We investigate opportunities and challenges for improving unsupervised machine learning using four common strategies with a long history in physics: divide-and-conquer, Occam's raz…
physics.comp-ph2017★ 1 cited
Nanophotonic Particle Simulation and Inverse Design Using Artificial Neural Networks
John Peurifoy, Yichen Shen, Li Jing +6
We propose a method to use artificial neural networks to approximate light scattering by multilayer nanoparticles. We find the network needs to be trained on only a small sampling…