{"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/feat-free-energy-estimators-with-adaptive","title":"FEAT: Free energy Estimators with Adaptive Transport","arxiv_id":"2504.11516","date":"2025-04-15","proceeding":null,"authors":["Jiajun He","Yuanqi Du","Francisco Vargas","Yuanqing Wang","Carla P. Gomes","José Miguel Hernández-Lobato","Eric Vanden-Eijnden"],"abstract":"We present Free energy Estimators with Adaptive Transport (FEAT), a novel framework for free energy estimation -- a critical challenge across scientific domains. FEAT leverages learned transports implemented via stochastic interpolants and provides consistent, minimum-variance estimators based on escorted Jarzynski equality and controlled Crooks theorem, alongside variational upper and lower bounds on free energy differences. Unifying equilibrium and non-equilibrium methods under a single theoretical framework, FEAT establishes a principled foundation for neural free energy calculations. Experimental validation on toy examples, molecular simulations, and quantum field theory demonstrates improvements over existing learning-based methods.","url_abs":"https://arxiv.org/abs/2504.11516v1","url_pdf":"https://arxiv.org/pdf/2504.11516v1.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":[],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2504.11516","atlas_url":"https://app.syntology.ai/?focus=2504.11516","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.11516"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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":"deterministic:regex_extraction","url":"https://github.com/jiajunhe98/FEAT","reach":null}],"summary":{"ran":1,"unverified":1},"by_repo_kind":{"found_in_text":{"samples":2,"ran":1,"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":"959385a6481e5fed","entry":"Coef","repo":"jiajunhe98/FEAT","repo_kind":"found_in_text","path":"loss.py","file_url":"https://github.com/jiajunhe98/FEAT/blob/HEAD/loss.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"959385a6481e5fed"}},{"code_sha256_prefix":"4e4aff9200097421","entry":"ti_loss","repo":"jiajunhe98/FEAT","repo_kind":"found_in_text","path":"loss.py","file_url":"https://github.com/jiajunhe98/FEAT/blob/HEAD/loss.py","link_basis":"first_harvest_node","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":"4e4aff9200097421"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}