Integration of Data and Theory for Accelerated Derivable Symbolic Discovery

• URL: http://arxiv.org/abs/2109.01634v1
• Date: Fri, 3 Sep 2021 17:19:17 GMT
• Title: Integration of Data and Theory for Accelerated Derivable Symbolic Discovery
• Authors: Cristina Cornelio, Sanjeeb Dash, Vernon Austel, Tyler Josephson, Joao Goncalves, Kenneth Clarkson, Nimrod Megiddo, Bachir El Khadir, Lior Horesh
• Abstract summary: We develop a methodology combining automated theorem proving with symbolic regression, enabling principled derivations of laws of nature. We demonstrate this for Kepler's third law, Einstein's relativistic time dilation, and Langmuir's theory of adsorbing. The combination of logical reasoning with machine learning provides generalizable insights into key aspects of the natural phenomena.
• Score: 3.7521856498259627
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scientists have long aimed to discover meaningful equations which accurately describe data. Machine learning algorithms automate construction of accurate data-driven models, but ensuring that these are consistent with existing knowledge is a challenge. We developed a methodology combining automated theorem proving with symbolic regression, enabling principled derivations of laws of nature. We demonstrate this for Kepler's third law, Einstein's relativistic time dilation, and Langmuir's theory of adsorption, in each case, automatically connecting experimental data with background theory. The combination of logical reasoning with machine learning provides generalizable insights into key aspects of the natural phenomena.

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