{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:DVJDZMK2ZAP54UGONANB24CRYQ","short_pith_number":"pith:DVJDZMK2","schema_version":"1.0","canonical_sha256":"1d523cb15ac81fde50ce681a1d7051c43883e3844b8b81f5ae234e4b0add09f5","source":{"kind":"arxiv","id":"1907.07587","version":2},"attestation_state":"computed","paper":{"title":"A Differentiable Programming System to Bridge Machine Learning and Scientific Computing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.PL","authors_text":"Alan Edelman, Chris Rackauckas, Elliot Saba, Keno Fischer, Mike Innes, Viral B Shah, Will Tebbutt","submitted_at":"2019-07-17T15:35:04Z","abstract_excerpt":"Scientific computing is increasingly incorporating the advancements in machine learning and the ability to work with large amounts of data. At the same time, machine learning models are becoming increasingly sophisticated and exhibit many features often seen in scientific computing, stressing the capabilities of machine learning frameworks. Just as the disciplines of scientific computing and machine learning have shared common underlying infrastructure in the form of numerical linear algebra, we now have the opportunity to further share new computational infrastructure, and thus ideas, in the "},"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":"1907.07587","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.PL","submitted_at":"2019-07-17T15:35:04Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"3d83a468e171cb2ac29a233413a8b4b00eb36013d22b47857fbde0fcf322f7ec","abstract_canon_sha256":"0fb32c192db239847631f3e399728488534c72e047ad16ca303f442ea35c6c8d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:40:16.837998Z","signature_b64":"U4PTVBpUKAnUL5hGkSu6YacQzncKs+nmpKMsVp0x8DJ3+63iUP9iVkhXauuouOznjx+BuD6pOIWysLMT2V+FDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1d523cb15ac81fde50ce681a1d7051c43883e3844b8b81f5ae234e4b0add09f5","last_reissued_at":"2026-05-17T23:40:16.837385Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:40:16.837385Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Differentiable Programming System to Bridge Machine Learning and Scientific Computing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.PL","authors_text":"Alan Edelman, Chris Rackauckas, Elliot Saba, Keno Fischer, Mike Innes, Viral B Shah, Will Tebbutt","submitted_at":"2019-07-17T15:35:04Z","abstract_excerpt":"Scientific computing is increasingly incorporating the advancements in machine learning and the ability to work with large amounts of data. At the same time, machine learning models are becoming increasingly sophisticated and exhibit many features often seen in scientific computing, stressing the capabilities of machine learning frameworks. Just as the disciplines of scientific computing and machine learning have shared common underlying infrastructure in the form of numerical linear algebra, we now have the opportunity to further share new computational infrastructure, and thus ideas, in the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.07587","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":""},"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":"1907.07587","created_at":"2026-05-17T23:40:16.837513+00:00"},{"alias_kind":"arxiv_version","alias_value":"1907.07587v2","created_at":"2026-05-17T23:40:16.837513+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.07587","created_at":"2026-05-17T23:40:16.837513+00:00"},{"alias_kind":"pith_short_12","alias_value":"DVJDZMK2ZAP5","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_16","alias_value":"DVJDZMK2ZAP54UGO","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_8","alias_value":"DVJDZMK2","created_at":"2026-05-18T12:33:15.570797+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":8,"internal_anchor_count":3,"sample":[{"citing_arxiv_id":"2605.22498","citing_title":"The Neural Compiler: Program-to-Network Translation for Hybrid Scientific Machine Learning","ref_index":11,"is_internal_anchor":true},{"citing_arxiv_id":"2604.20031","citing_title":"Decision-Focused Federated Learning Under Heterogeneous Objectives and Constraints","ref_index":2,"is_internal_anchor":true},{"citing_arxiv_id":"2001.04385","citing_title":"Universal Differential Equations for Scientific Machine Learning","ref_index":52,"is_internal_anchor":true},{"citing_arxiv_id":"2604.20031","citing_title":"Decision-Focused Federated Learning Under Heterogeneous Objectives and Constraints","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2604.13291","citing_title":"Physics-informed reservoir characterization from bulk and extreme pressure events with a differentiable simulator","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2604.06896","citing_title":"VertAX: a differentiable vertex model for learning epithelial tissue mechanics","ref_index":30,"is_internal_anchor":false},{"citing_arxiv_id":"2604.06425","citing_title":"Neural Computers","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2604.16784","citing_title":"Learning Non-Markovian Noise via Ensemble Optimal Control","ref_index":38,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DVJDZMK2ZAP54UGONANB24CRYQ","json":"https://pith.science/pith/DVJDZMK2ZAP54UGONANB24CRYQ.json","graph_json":"https://pith.science/api/pith-number/DVJDZMK2ZAP54UGONANB24CRYQ/graph.json","events_json":"https://pith.science/api/pith-number/DVJDZMK2ZAP54UGONANB24CRYQ/events.json","paper":"https://pith.science/paper/DVJDZMK2"},"agent_actions":{"view_html":"https://pith.science/pith/DVJDZMK2ZAP54UGONANB24CRYQ","download_json":"https://pith.science/pith/DVJDZMK2ZAP54UGONANB24CRYQ.json","view_paper":"https://pith.science/paper/DVJDZMK2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1907.07587&json=true","fetch_graph":"https://pith.science/api/pith-number/DVJDZMK2ZAP54UGONANB24CRYQ/graph.json","fetch_events":"https://pith.science/api/pith-number/DVJDZMK2ZAP54UGONANB24CRYQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DVJDZMK2ZAP54UGONANB24CRYQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DVJDZMK2ZAP54UGONANB24CRYQ/action/storage_attestation","attest_author":"https://pith.science/pith/DVJDZMK2ZAP54UGONANB24CRYQ/action/author_attestation","sign_citation":"https://pith.science/pith/DVJDZMK2ZAP54UGONANB24CRYQ/action/citation_signature","submit_replication":"https://pith.science/pith/DVJDZMK2ZAP54UGONANB24CRYQ/action/replication_record"}},"created_at":"2026-05-17T23:40:16.837513+00:00","updated_at":"2026-05-17T23:40:16.837513+00:00"}