Active Learning in Symbolic Regression with Physical Constraints
- URL: http://arxiv.org/abs/2305.10379v2
- Date: Fri, 19 May 2023 02:27:57 GMT
- Title: Active Learning in Symbolic Regression with Physical Constraints
- Authors: Jorge Medina, Andrew D. White
- Abstract summary: Evolutionary symbolic regression (SR) fits a symbolic equation to data, which gives a concise interpretable model.
We explore using SR as a method to propose which data to gather in an active learning setting with physical constraints.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Evolutionary symbolic regression (SR) fits a symbolic equation to data, which
gives a concise interpretable model. We explore using SR as a method to propose
which data to gather in an active learning setting with physical constraints.
SR with active learning proposes which experiments to do next. Active learning
is done with query by committee, where the Pareto frontier of equations is the
committee. The physical constraints improve proposed equations in very low data
settings. These approaches reduce the data required for SR and achieves state
of the art results in data required to rediscover known equations.
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