{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:KT4UYVVGUGJW3XU63DZCHXMPQ3","short_pith_number":"pith:KT4UYVVG","schema_version":"1.0","canonical_sha256":"54f94c56a6a1936dde9ed8f223dd8f86c9efc800a8373b299ee315d17746e957","source":{"kind":"arxiv","id":"2202.00728","version":1},"attestation_state":"computed","paper":{"title":"Physical Design using Differentiable Learned Simulators","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Alvaro Sanchez-Gonzalez, Jessica Hamrick, Kelsey R. Allen, Kimberly Stachenfeld, Peter Battaglia, Tatiana Lopez-Guevara, Tobias Pfaff","submitted_at":"2022-02-01T19:56:39Z","abstract_excerpt":"Designing physical artifacts that serve a purpose - such as tools and other functional structures - is central to engineering as well as everyday human behavior. Though automating design has tremendous promise, general-purpose methods do not yet exist. Here we explore a simple, fast, and robust approach to inverse design which combines learned forward simulators based on graph neural networks with gradient-based design optimization. Our approach solves high-dimensional problems with complex physical dynamics, including designing surfaces and tools to manipulate fluid flows and optimizing the s"},"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":"2202.00728","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-02-01T19:56:39Z","cross_cats_sorted":[],"title_canon_sha256":"06917412ed866dedf04f021fd17f5bd26f0a7402df64637ac8732188214e9278","abstract_canon_sha256":"42c49ee0bf9b87dc3c7eb8e57bacff17f03be431e417c4fcc5119605714a5b15"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:53:26.817441Z","signature_b64":"vAhsT6WXZYUnQe/jlks2noMjPNbqi0bqaOg2hGJjdtqOnVJYwyFM5qt7KaBnUs5dritqHIXNfyD6xV90WiPmDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"54f94c56a6a1936dde9ed8f223dd8f86c9efc800a8373b299ee315d17746e957","last_reissued_at":"2026-07-05T03:53:26.817014Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:53:26.817014Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Physical Design using Differentiable Learned Simulators","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Alvaro Sanchez-Gonzalez, Jessica Hamrick, Kelsey R. Allen, Kimberly Stachenfeld, Peter Battaglia, Tatiana Lopez-Guevara, Tobias Pfaff","submitted_at":"2022-02-01T19:56:39Z","abstract_excerpt":"Designing physical artifacts that serve a purpose - such as tools and other functional structures - is central to engineering as well as everyday human behavior. Though automating design has tremendous promise, general-purpose methods do not yet exist. Here we explore a simple, fast, and robust approach to inverse design which combines learned forward simulators based on graph neural networks with gradient-based design optimization. Our approach solves high-dimensional problems with complex physical dynamics, including designing surfaces and tools to manipulate fluid flows and optimizing the s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.00728","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/2202.00728/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":"2202.00728","created_at":"2026-07-05T03:53:26.817073+00:00"},{"alias_kind":"arxiv_version","alias_value":"2202.00728v1","created_at":"2026-07-05T03:53:26.817073+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.00728","created_at":"2026-07-05T03:53:26.817073+00:00"},{"alias_kind":"pith_short_12","alias_value":"KT4UYVVGUGJW","created_at":"2026-07-05T03:53:26.817073+00:00"},{"alias_kind":"pith_short_16","alias_value":"KT4UYVVGUGJW3XU6","created_at":"2026-07-05T03:53:26.817073+00:00"},{"alias_kind":"pith_short_8","alias_value":"KT4UYVVG","created_at":"2026-07-05T03:53:26.817073+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2602.10712","citing_title":"Photons x Force: Differentiable Radiation Pressure Modeling","ref_index":1,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KT4UYVVGUGJW3XU63DZCHXMPQ3","json":"https://pith.science/pith/KT4UYVVGUGJW3XU63DZCHXMPQ3.json","graph_json":"https://pith.science/api/pith-number/KT4UYVVGUGJW3XU63DZCHXMPQ3/graph.json","events_json":"https://pith.science/api/pith-number/KT4UYVVGUGJW3XU63DZCHXMPQ3/events.json","paper":"https://pith.science/paper/KT4UYVVG"},"agent_actions":{"view_html":"https://pith.science/pith/KT4UYVVGUGJW3XU63DZCHXMPQ3","download_json":"https://pith.science/pith/KT4UYVVGUGJW3XU63DZCHXMPQ3.json","view_paper":"https://pith.science/paper/KT4UYVVG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2202.00728&json=true","fetch_graph":"https://pith.science/api/pith-number/KT4UYVVGUGJW3XU63DZCHXMPQ3/graph.json","fetch_events":"https://pith.science/api/pith-number/KT4UYVVGUGJW3XU63DZCHXMPQ3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KT4UYVVGUGJW3XU63DZCHXMPQ3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KT4UYVVGUGJW3XU63DZCHXMPQ3/action/storage_attestation","attest_author":"https://pith.science/pith/KT4UYVVGUGJW3XU63DZCHXMPQ3/action/author_attestation","sign_citation":"https://pith.science/pith/KT4UYVVGUGJW3XU63DZCHXMPQ3/action/citation_signature","submit_replication":"https://pith.science/pith/KT4UYVVGUGJW3XU63DZCHXMPQ3/action/replication_record"}},"created_at":"2026-07-05T03:53:26.817073+00:00","updated_at":"2026-07-05T03:53:26.817073+00:00"}