Machine learning symbolic equations for diffusion with physics-based descriptions
dc.creator | Papastamatiou K., Sofos F., Karakasidis T.E. | en |
dc.date.accessioned | 2023-01-31T09:44:25Z | |
dc.date.available | 2023-01-31T09:44:25Z | |
dc.date.issued | 2022 | |
dc.identifier | 10.1063/5.0082147 | |
dc.identifier.issn | 21583226 | |
dc.identifier.uri | http://hdl.handle.net/11615/77817 | |
dc.description.abstract | This work incorporates symbolic regression to propose simple and accurate expressions that fit to material datasets. The incorporation of symbolic regression in physical sciences opens the way to replace "black-box"machine learning techniques with representations that carry the physical meaning and can reveal the underlying mechanism in a purely data-driven approach. The application here is the extraction of analytical equations for the self-diffusion coefficient of the Lennard-Jones fluid by exploiting widely incorporating data from the literature. We propose symbolic formulas of low complexity and error that achieve better or comparable results to well-known microscopic and empirical expressions. Results refer to the material state space both as a whole and in distinct gas, liquid, and supercritical regions. © 2022 Author(s). | en |
dc.language.iso | en | en |
dc.source | AIP Advances | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124162084&doi=10.1063%2f5.0082147&partnerID=40&md5=0f974f05da2546e38cd0971ccf784fac | |
dc.subject | Diffusion in liquids | en |
dc.subject | Analytical equations | en |
dc.subject | Black boxes | en |
dc.subject | Data-driven approach | en |
dc.subject | Machine learning techniques | en |
dc.subject | Physical meanings | en |
dc.subject | Physical science | en |
dc.subject | Physics-based | en |
dc.subject | Simple++ | en |
dc.subject | Symbolic equations | en |
dc.subject | Symbolic regression | en |
dc.subject | Machine learning | en |
dc.subject | American Institute of Physics Inc. | en |
dc.title | Machine learning symbolic equations for diffusion with physics-based descriptions | en |
dc.type | journalArticle | en |
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