Name
Abstract | ||
|---|---|---|
A core challenge for both physics and artificial intelligence (Al) 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 symmetries, separability, compositionality, and other simplifying properties. In this spirit, we develop a recursive multidimensional symbolic regression algorithm that combines neural network fitting with a suite of physics-inspired techniques. 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 physics-based test set, we improve the state-of-the-art success rate from 15 to 90%. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1126/sciadv.aay2631 | SCIENCE ADVANCES |
DocType | Volume | Issue |
Journal | 6 | 16 |
ISSN | Citations | PageRank |
2375-2548 | 5 | 0.55 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Silviu-Marian Udrescu | 1 | 5 | 0.89 |
| Max Tegmark | 2 | 160 | 16.27 |