{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:HWUC6YLHTDJY2URNPF6XKCVJCU","short_pith_number":"pith:HWUC6YLH","schema_version":"1.0","canonical_sha256":"3da82f616798d38d522d797d750aa9150418cb577791fd67ab8af8131afbf10b","source":{"kind":"arxiv","id":"2302.13682","version":2},"attestation_state":"computed","paper":{"title":"A deep learning approach to the measurement of long-lived memory kernels from Generalised Langevin Dynamics","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cond-mat.soft","cond-mat.stat-mech"],"primary_cat":"cond-mat.dis-nn","authors_text":"Ilian Pihlajamaa, Liesbeth M. C. Janssen, Max Kerr Winter, Vincent E. Debets","submitted_at":"2023-02-27T11:38:25Z","abstract_excerpt":"Memory effects are ubiquitous in a wide variety of complex physical phenomena, ranging from glassy dynamics and metamaterials to climate models. The Generalised Langevin Equation (GLE) provides a rigorous way to describe memory effects via the so-called memory kernel in an integro-differential equation. However, the memory kernel is often unknown, and accurately predicting or measuring it via e.g. a numerical inverse Laplace transform remains a herculean task. Here we describe a novel method using deep neural networks (DNNs) to measure memory kernels from dynamical data. As proof-of-principle,"},"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":"2302.13682","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.dis-nn","submitted_at":"2023-02-27T11:38:25Z","cross_cats_sorted":["cond-mat.soft","cond-mat.stat-mech"],"title_canon_sha256":"e47ad94cf635cbfaeee889ac56d19890c6ff3c4e3674b2c9cb823f1b8e52a01c","abstract_canon_sha256":"c3b588722b76da11614286c4bf67cb91cbd4e51b719f6711f410e27a5ea0ce31"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:25:39.891090Z","signature_b64":"H82/4bMZhv0FDjObLxZw7xrrsdn+kuwbxa2G3cm1C8+Mou4AbIRQ0R91ERTjKVQDALur3sdM8mbmc3Fm6qlcCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3da82f616798d38d522d797d750aa9150418cb577791fd67ab8af8131afbf10b","last_reissued_at":"2026-07-05T06:25:39.890546Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:25:39.890546Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A deep learning approach to the measurement of long-lived memory kernels from Generalised Langevin Dynamics","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cond-mat.soft","cond-mat.stat-mech"],"primary_cat":"cond-mat.dis-nn","authors_text":"Ilian Pihlajamaa, Liesbeth M. C. Janssen, Max Kerr Winter, Vincent E. Debets","submitted_at":"2023-02-27T11:38:25Z","abstract_excerpt":"Memory effects are ubiquitous in a wide variety of complex physical phenomena, ranging from glassy dynamics and metamaterials to climate models. The Generalised Langevin Equation (GLE) provides a rigorous way to describe memory effects via the so-called memory kernel in an integro-differential equation. However, the memory kernel is often unknown, and accurately predicting or measuring it via e.g. a numerical inverse Laplace transform remains a herculean task. Here we describe a novel method using deep neural networks (DNNs) to measure memory kernels from dynamical data. As proof-of-principle,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.13682","kind":"arxiv","version":2},"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/2302.13682/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":"2302.13682","created_at":"2026-07-05T06:25:39.890614+00:00"},{"alias_kind":"arxiv_version","alias_value":"2302.13682v2","created_at":"2026-07-05T06:25:39.890614+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.13682","created_at":"2026-07-05T06:25:39.890614+00:00"},{"alias_kind":"pith_short_12","alias_value":"HWUC6YLHTDJY","created_at":"2026-07-05T06:25:39.890614+00:00"},{"alias_kind":"pith_short_16","alias_value":"HWUC6YLHTDJY2URN","created_at":"2026-07-05T06:25:39.890614+00:00"},{"alias_kind":"pith_short_8","alias_value":"HWUC6YLH","created_at":"2026-07-05T06:25:39.890614+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/HWUC6YLHTDJY2URNPF6XKCVJCU","json":"https://pith.science/pith/HWUC6YLHTDJY2URNPF6XKCVJCU.json","graph_json":"https://pith.science/api/pith-number/HWUC6YLHTDJY2URNPF6XKCVJCU/graph.json","events_json":"https://pith.science/api/pith-number/HWUC6YLHTDJY2URNPF6XKCVJCU/events.json","paper":"https://pith.science/paper/HWUC6YLH"},"agent_actions":{"view_html":"https://pith.science/pith/HWUC6YLHTDJY2URNPF6XKCVJCU","download_json":"https://pith.science/pith/HWUC6YLHTDJY2URNPF6XKCVJCU.json","view_paper":"https://pith.science/paper/HWUC6YLH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2302.13682&json=true","fetch_graph":"https://pith.science/api/pith-number/HWUC6YLHTDJY2URNPF6XKCVJCU/graph.json","fetch_events":"https://pith.science/api/pith-number/HWUC6YLHTDJY2URNPF6XKCVJCU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HWUC6YLHTDJY2URNPF6XKCVJCU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HWUC6YLHTDJY2URNPF6XKCVJCU/action/storage_attestation","attest_author":"https://pith.science/pith/HWUC6YLHTDJY2URNPF6XKCVJCU/action/author_attestation","sign_citation":"https://pith.science/pith/HWUC6YLHTDJY2URNPF6XKCVJCU/action/citation_signature","submit_replication":"https://pith.science/pith/HWUC6YLHTDJY2URNPF6XKCVJCU/action/replication_record"}},"created_at":"2026-07-05T06:25:39.890614+00:00","updated_at":"2026-07-05T06:25:39.890614+00:00"}