{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/scimed-a-computational-framework-for-physics","title":"A computational framework for physics-informed symbolic regression with straightforward integration of domain knowledge","arxiv_id":"2209.06257","date":"2022-09-13","proceeding":null,"authors":["Liron Simon Keren","Alex Liberzon","Teddy Lazebnik"],"abstract":"Discovering a meaningful symbolic expression that explains experimental data is a fundamental challenge in many scientific fields. We present a novel, open-source computational framework called Scientist-Machine Equation Detector (SciMED), which integrates scientific discipline wisdom in a scientist-in-the-loop approach, with state-of-the-art symbolic regression (SR) methods. SciMED combines a wrapper selection method, that is based on a genetic algorithm, with automatic machine learning and two levels of SR methods. We test SciMED on five configurations of a settling sphere, with and without aerodynamic non-linear drag force, and with excessive noise in the measurements. We show that SciMED is sufficiently robust to discover the correct physically meaningful symbolic expressions from the data, and demonstrate how the integration of domain knowledge enhances its performance. Our results indicate better performance on these tasks than the state-of-the-art SR software packages , even in cases where no knowledge is integrated. Moreover, we demonstrate how SciMED can alert the user about possible missing features, unlike the majority of current SR systems.","url_abs":"https://arxiv.org/abs/2209.06257v3","url_pdf":"https://arxiv.org/pdf/2209.06257v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"scimed-a-computational-framework-for-physics","repo_url":"https://github.com/lironsimon/scimed","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"symbolic-regression","task_name":"Symbolic Regression"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[{"method_slug":"test","method_name":"Test"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2209.06257","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.06257"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/lironsimon/scimed","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":5},"by_repo_kind":{"official":{"samples":5,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"86d31a687f794a34","entry":"add","repo":"lironsimon/scimed","repo_kind":"official","path":"algo/ebs/eq_functions.py","file_url":"https://github.com/lironsimon/scimed/blob/HEAD/algo/ebs/eq_functions.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"86d31a687f794a34"}},{"code_sha256_prefix":"6300eacee94f7631","entry":"better_symbolic_reg_fitness","repo":"lironsimon/scimed","repo_kind":"official","path":"utills/fitness_methods.py","file_url":"https://github.com/lironsimon/scimed/blob/HEAD/utills/fitness_methods.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"6300eacee94f7631"}},{"code_sha256_prefix":"1deef6aba037a1dc","entry":"div","repo":"lironsimon/scimed","repo_kind":"official","path":"algo/ebs/eq_functions.py","file_url":"https://github.com/lironsimon/scimed/blob/HEAD/algo/ebs/eq_functions.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"1deef6aba037a1dc"}},{"code_sha256_prefix":"c4a83c0c0edbb4b0","entry":"simple_symbolic_reg_fitness","repo":"lironsimon/scimed","repo_kind":"official","path":"utills/fitness_methods.py","file_url":"https://github.com/lironsimon/scimed/blob/HEAD/utills/fitness_methods.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"c4a83c0c0edbb4b0"}},{"code_sha256_prefix":"a51fe0ed96d05ea9","entry":"sub","repo":"lironsimon/scimed","repo_kind":"official","path":"algo/ebs/eq_functions.py","file_url":"https://github.com/lironsimon/scimed/blob/HEAD/algo/ebs/eq_functions.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"a51fe0ed96d05ea9"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}