{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:56I2ZTIFHWNXFDMIM7B4PMU46J","short_pith_number":"pith:56I2ZTIF","schema_version":"1.0","canonical_sha256":"ef91accd053d9b728d8867c3c7b29cf24c0ad239d0bbbfaf8f89fd4d2ba74776","source":{"kind":"arxiv","id":"2312.03166","version":1},"attestation_state":"computed","paper":{"title":"Deep Learning for Fast Inference of Mechanistic Models' Parameters","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["q-bio.QM"],"primary_cat":"cs.LG","authors_text":"Mariano Nicolas Cruz-Bournazou, Maxim Borisyak, Peter Neubauer, Stefan Born","submitted_at":"2023-12-05T22:16:54Z","abstract_excerpt":"Inferring parameters of macro-kinetic growth models, typically represented by Ordinary Differential Equations (ODE), from the experimental data is a crucial step in bioprocess engineering. Conventionally, estimates of the parameters are obtained by fitting the mechanistic model to observations. Fitting, however, requires a significant computational power. Specifically, during the development of new bioprocesses that use previously unknown organisms or strains, efficient, robust, and computationally cheap methods for parameter estimation are of great value. In this work, we propose using Deep N"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2312.03166","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-12-05T22:16:54Z","cross_cats_sorted":["q-bio.QM"],"title_canon_sha256":"9daf770192902f0d86fccf11dccec0778e5ac58319b37de786242b3f7ffd7b8e","abstract_canon_sha256":"abcaadb1925493cf5f6fb42b427920e3a2995ae11f80b443b6b6bea513de547b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:20:59.408036Z","signature_b64":"M/qooTNIZScBHu14srsiefRUCADhYLh/Mn9AVuMcU3LBzlJxXp1FOQj+swBq93fd7aMwbGnqgfAq9RmArDihDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ef91accd053d9b728d8867c3c7b29cf24c0ad239d0bbbfaf8f89fd4d2ba74776","last_reissued_at":"2026-07-05T07:20:59.407561Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:20:59.407561Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Learning for Fast Inference of Mechanistic Models' Parameters","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["q-bio.QM"],"primary_cat":"cs.LG","authors_text":"Mariano Nicolas Cruz-Bournazou, Maxim Borisyak, Peter Neubauer, Stefan Born","submitted_at":"2023-12-05T22:16:54Z","abstract_excerpt":"Inferring parameters of macro-kinetic growth models, typically represented by Ordinary Differential Equations (ODE), from the experimental data is a crucial step in bioprocess engineering. Conventionally, estimates of the parameters are obtained by fitting the mechanistic model to observations. Fitting, however, requires a significant computational power. Specifically, during the development of new bioprocesses that use previously unknown organisms or strains, efficient, robust, and computationally cheap methods for parameter estimation are of great value. In this work, we propose using Deep N"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.03166","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2312.03166/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2312.03166","created_at":"2026-07-05T07:20:59.407618+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.03166v1","created_at":"2026-07-05T07:20:59.407618+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.03166","created_at":"2026-07-05T07:20:59.407618+00:00"},{"alias_kind":"pith_short_12","alias_value":"56I2ZTIFHWNX","created_at":"2026-07-05T07:20:59.407618+00:00"},{"alias_kind":"pith_short_16","alias_value":"56I2ZTIFHWNXFDMI","created_at":"2026-07-05T07:20:59.407618+00:00"},{"alias_kind":"pith_short_8","alias_value":"56I2ZTIF","created_at":"2026-07-05T07:20:59.407618+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/56I2ZTIFHWNXFDMIM7B4PMU46J","json":"https://pith.science/pith/56I2ZTIFHWNXFDMIM7B4PMU46J.json","graph_json":"https://pith.science/api/pith-number/56I2ZTIFHWNXFDMIM7B4PMU46J/graph.json","events_json":"https://pith.science/api/pith-number/56I2ZTIFHWNXFDMIM7B4PMU46J/events.json","paper":"https://pith.science/paper/56I2ZTIF"},"agent_actions":{"view_html":"https://pith.science/pith/56I2ZTIFHWNXFDMIM7B4PMU46J","download_json":"https://pith.science/pith/56I2ZTIFHWNXFDMIM7B4PMU46J.json","view_paper":"https://pith.science/paper/56I2ZTIF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.03166&json=true","fetch_graph":"https://pith.science/api/pith-number/56I2ZTIFHWNXFDMIM7B4PMU46J/graph.json","fetch_events":"https://pith.science/api/pith-number/56I2ZTIFHWNXFDMIM7B4PMU46J/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/56I2ZTIFHWNXFDMIM7B4PMU46J/action/timestamp_anchor","attest_storage":"https://pith.science/pith/56I2ZTIFHWNXFDMIM7B4PMU46J/action/storage_attestation","attest_author":"https://pith.science/pith/56I2ZTIFHWNXFDMIM7B4PMU46J/action/author_attestation","sign_citation":"https://pith.science/pith/56I2ZTIFHWNXFDMIM7B4PMU46J/action/citation_signature","submit_replication":"https://pith.science/pith/56I2ZTIFHWNXFDMIM7B4PMU46J/action/replication_record"}},"created_at":"2026-07-05T07:20:59.407618+00:00","updated_at":"2026-07-05T07:20:59.407618+00:00"}