Interpretable scientific discovery with symbolic regression: a review
<p dir="ltr">Symbolic regression is emerging as a promising machine learning method for learning succinct underlying interpretable mathematical expressions directly from data. Whereas it has been traditionally tackled with genetic programming, it has recently gained a growing interes...
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2024
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| _version_ | 1864513510712541184 |
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| author | Nour Makke (19160749) |
| author2 | Sanjay Chawla (4254202) |
| author2_role | author |
| author_facet | Nour Makke (19160749) Sanjay Chawla (4254202) |
| author_role | author |
| dc.creator.none.fl_str_mv | Nour Makke (19160749) Sanjay Chawla (4254202) |
| dc.date.none.fl_str_mv | 2024-01-02T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s10462-023-10622-0 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Interpretable_scientific_discovery_with_symbolic_regression_a_review/26317009 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Data management and data science Machine learning Software engineering Mathematical sciences Symbolic Regression Automated Scientific Discovery Interpretable AI |
| dc.title.none.fl_str_mv | Interpretable scientific discovery with symbolic regression: a review |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Symbolic regression is emerging as a promising machine learning method for learning succinct underlying interpretable mathematical expressions directly from data. Whereas it has been traditionally tackled with genetic programming, it has recently gained a growing interest in deep learning as a data-driven model discovery tool, achieving significant advances in various application domains ranging from fundamental to applied sciences. In this survey, we present a structured and comprehensive overview of symbolic regression methods, review the adoption of these methods for model discovery in various areas, and assess their effectiveness. We have also grouped state-of-the-art symbolic regression applications in a categorized manner in a living review.</p><h2>Other Information</h2><p dir="ltr">Published in: Artificial Intelligence Review<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s10462-023-10622-0" target="_blank">https://dx.doi.org/10.1007/s10462-023-10622-0</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_2ec700a5125dfad7b5317a535110da79 |
| identifier_str_mv | 10.1007/s10462-023-10622-0 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26317009 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Interpretable scientific discovery with symbolic regression: a reviewNour Makke (19160749)Sanjay Chawla (4254202)Information and computing sciencesArtificial intelligenceData management and data scienceMachine learningSoftware engineeringMathematical sciencesSymbolic RegressionAutomated Scientific DiscoveryInterpretable AI<p dir="ltr">Symbolic regression is emerging as a promising machine learning method for learning succinct underlying interpretable mathematical expressions directly from data. Whereas it has been traditionally tackled with genetic programming, it has recently gained a growing interest in deep learning as a data-driven model discovery tool, achieving significant advances in various application domains ranging from fundamental to applied sciences. In this survey, we present a structured and comprehensive overview of symbolic regression methods, review the adoption of these methods for model discovery in various areas, and assess their effectiveness. We have also grouped state-of-the-art symbolic regression applications in a categorized manner in a living review.</p><h2>Other Information</h2><p dir="ltr">Published in: Artificial Intelligence Review<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s10462-023-10622-0" target="_blank">https://dx.doi.org/10.1007/s10462-023-10622-0</a></p>2024-01-02T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s10462-023-10622-0https://figshare.com/articles/journal_contribution/Interpretable_scientific_discovery_with_symbolic_regression_a_review/26317009CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/263170092024-01-02T03:00:00Z |
| spellingShingle | Interpretable scientific discovery with symbolic regression: a review Nour Makke (19160749) Information and computing sciences Artificial intelligence Data management and data science Machine learning Software engineering Mathematical sciences Symbolic Regression Automated Scientific Discovery Interpretable AI |
| status_str | publishedVersion |
| title | Interpretable scientific discovery with symbolic regression: a review |
| title_full | Interpretable scientific discovery with symbolic regression: a review |
| title_fullStr | Interpretable scientific discovery with symbolic regression: a review |
| title_full_unstemmed | Interpretable scientific discovery with symbolic regression: a review |
| title_short | Interpretable scientific discovery with symbolic regression: a review |
| title_sort | Interpretable scientific discovery with symbolic regression: a review |
| topic | Information and computing sciences Artificial intelligence Data management and data science Machine learning Software engineering Mathematical sciences Symbolic Regression Automated Scientific Discovery Interpretable AI |