AI Feynman: A physics-inspired method for symbolic regression
© 2020 The Authors. A core challenge for both physics and artificial intelligence (AI) is symbolic regression: Finding a symbolic expression that matches data from an unknown function. Although this problem is likely to be NP-hard in principle, functions of practical interest often exhibit symmetrie...
Main Authors: | Udrescu, Silviu-Marian, Tegmark, Max |
---|---|
Format: | Article |
Language: | English |
Published: |
American Association for the Advancement of Science (AAAS)
2021
|
Online Access: | https://hdl.handle.net/1721.1/132379 |
Similar Items
-
AI Feynman: A physics-inspired method for symbolic regression
by: Udrescu, Silviu-Marian, et al.
Published: (2022) -
Symbolic pregression: Discovering physical laws from distorted video
by: Udrescu, Silviu-Marian, et al.
Published: (2022) -
Radioactive Atoms and Molecules for Fundamental Physics
by: Udrescu, Silviu-Marian
Published: (2024) -
Quasi anomalous knowledge: searching for new physics with embedded knowledge
by: Park, Sang E., et al.
Published: (2021) -
Quasi anomalous knowledge: searching for new physics with embedded knowledge
by: Park, Sang E., et al.
Published: (2022)