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dmmBy Silviu-Marian Udrescu and Max Tegmark: arxiv.org/abs/1905.11481
github.com/SJ001/AI-Feynman
"recursive multidimensional symbolic regression algorithm that combines neural network fitting with a suite of physics-inspired techniques."
Neural networks are used to discover "symmetries, separability, compositionality and other simplifying properties" and thus reduce the search space.
"We apply it to 100 equations from the Feynman Lectures on Physics, and it discovers all of them, while previous publicly available software cracks only 71; for a more difficult test set, we improve the state of the art success rate from 15% to 90%."
Follow-up by the same authors: "Symbolic Pregression: Discovering Physical Laws from Raw Distorted Video": arxiv.org/abs/2005.11212
github.com/SJ001/AI-Feynman
"recursive multidimensional symbolic regression algorithm that combines neural network fitting with a suite of physics-inspired techniques."
Neural networks are used to discover "symmetries, separability, compositionality and other simplifying properties" and thus reduce the search space.
"We apply it to 100 equations from the Feynman Lectures on Physics, and it discovers all of them, while previous publicly available software cracks only 71; for a more difficult test set, we improve the state of the art success rate from 15% to 90%."
Follow-up by the same authors: "Symbolic Pregression: Discovering Physical Laws from Raw Distorted Video": arxiv.org/abs/2005.11212
no subject
Date: 2021-12-04 02:19 pm (UTC)https://arxiv.org/abs/2006.10782