{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:ZLF6NH2N4AGTMDGFOPSZ3AX45K","short_pith_number":"pith:ZLF6NH2N","canonical_record":{"source":{"id":"2607.13174","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.comp-ph","submitted_at":"2026-07-14T18:23:26Z","cross_cats_sorted":["cs.CE","cs.RO"],"title_canon_sha256":"e67b5a3b58a236f2f61ed5aa10bee234e7352e35e9f1a718a3debc9a3df80dc9","abstract_canon_sha256":"c8b19cff6e47197e7d288ee71e084c40338c714de78cbe24d249eb65b0217594"},"schema_version":"1.0"},"canonical_sha256":"cacbe69f4de00d360cc573e59d82fceab8f776e9557f967c9b934e262e10df1e","source":{"kind":"arxiv","id":"2607.13174","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.13174","created_at":"2026-07-16T00:22:01Z"},{"alias_kind":"arxiv_version","alias_value":"2607.13174v1","created_at":"2026-07-16T00:22:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.13174","created_at":"2026-07-16T00:22:01Z"},{"alias_kind":"pith_short_12","alias_value":"ZLF6NH2N4AGT","created_at":"2026-07-16T00:22:01Z"},{"alias_kind":"pith_short_16","alias_value":"ZLF6NH2N4AGTMDGF","created_at":"2026-07-16T00:22:01Z"},{"alias_kind":"pith_short_8","alias_value":"ZLF6NH2N","created_at":"2026-07-16T00:22:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:ZLF6NH2N4AGTMDGFOPSZ3AX45K","target":"record","payload":{"canonical_record":{"source":{"id":"2607.13174","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.comp-ph","submitted_at":"2026-07-14T18:23:26Z","cross_cats_sorted":["cs.CE","cs.RO"],"title_canon_sha256":"e67b5a3b58a236f2f61ed5aa10bee234e7352e35e9f1a718a3debc9a3df80dc9","abstract_canon_sha256":"c8b19cff6e47197e7d288ee71e084c40338c714de78cbe24d249eb65b0217594"},"schema_version":"1.0"},"canonical_sha256":"cacbe69f4de00d360cc573e59d82fceab8f776e9557f967c9b934e262e10df1e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-16T00:22:01.183494Z","signature_b64":"Y1mxomWf1/aThrelg9cwKl/+rr600PE6qOHmDP4RNF8CNSKNMEUwghXVNf+yYfg6gbgjOUKXEjA6xke+RIIGAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cacbe69f4de00d360cc573e59d82fceab8f776e9557f967c9b934e262e10df1e","last_reissued_at":"2026-07-16T00:22:01.182519Z","signature_status":"signed_v1","first_computed_at":"2026-07-16T00:22:01.182519Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2607.13174","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-16T00:22:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"j57frjqpPNX91uZFnleeZR6FSlft3XHt/IG5l5KYIMAl4Eyt47ZOlyfvmkOWsbwLxgNyXd4XJQTqm+LkA0RdCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T18:43:36.807100Z"},"content_sha256":"e7d15cf52f4e9a3c980f69699e1763d1b28df01034c665a346357a58705f5ac8","schema_version":"1.0","event_id":"sha256:e7d15cf52f4e9a3c980f69699e1763d1b28df01034c665a346357a58705f5ac8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:ZLF6NH2N4AGTMDGFOPSZ3AX45K","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Towards end-to-end optimization in multimaterial 3D printing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CE","cs.RO"],"primary_cat":"physics.comp-ph","authors_text":"Jingye Tan, Nikolaos Bouklas, Noy Cohen, Robert F. Shepherd, Steven Yang, Xue-Ling Luo","submitted_at":"2026-07-14T18:23:26Z","abstract_excerpt":"Multimaterial 3D printing enables the fabrication of functionally graded components, but optimizing their spatial material distribution alongside structural topology remains a formidable challenge due to high-dimensional design spaces and complex constitutive modeling. This paper presents an end-to-end computational framework integrating sparsified physics-augmented neural networks with finite-element-based topology optimization. By extracting closed-form, composition-aware hyperelastic constitutive laws from experimental data, this approach facilitates exact symbolic differentiation via the a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.13174","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/2607.13174/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-16T00:22:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QtheeD2mWYfQjBn37YsP1HdfQqXsRK24uUGf1adnkLbaINo65AB7ai3e1OiaYsd237Vc8CSxnlBq5jDs/RigDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T18:43:36.810614Z"},"content_sha256":"1c7748bdb6c5dbcd66677e82c61f23644d90faab2cf6b61068938bd9e019907b","schema_version":"1.0","event_id":"sha256:1c7748bdb6c5dbcd66677e82c61f23644d90faab2cf6b61068938bd9e019907b"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:ZLF6NH2N4AGTMDGFOPSZ3AX45K","target":"integrity","payload":{"note":"DOI is split by whitespace or line breaks in the printed bibliography. Reconstructed DOI 10.1002/nme.1620240207 resolves to 'The method of moving asymptotes—a new method for structural optimization'. A reader following the printed text alone cannot reach it.","snippet":"K. Svanberg, The method of moving asymptotes—a new method for structural optimization, Inter- national Journal for Numerical Methods in Engineering 24 (2) (1987) 359–373.doi:10.1002/nme. 1620240207","arxiv_id":"2607.13174","detector":"doi_compliance","evidence":{"ref_index":59,"verdict_class":"incontrovertible","resolved_title":"The method of moving asymptotes—a new method for structural optimization","printed_excerpt":"10.1002/nme","reconstructed_doi":"10.1002/nme.1620240207"},"severity":"advisory","ref_index":59,"audited_at":"2026-08-02T06:11:08.375498Z","event_type":"pith.integrity.v1","detected_doi":"10.1002/nme.1620240207","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"recoverable_identifier","evidence_hash":"a9c80039ae1fd14fc5eda9f857c410a2929abbd1f26fabb6c2ce40d1479eeda4","paper_version":1,"verdict_class":"incontrovertible","resolved_title":"The method of moving asymptotes—a new method for structural optimization","detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":17333,"payload_sha256":"d0805f4b5e9d84b54c96276a176a337ad7826dd72463c472928071098347eed9","signature_b64":"9cOeICze1445hZ5Nrfv6BXYANb5iZvqVMe1uCqM9iPQU3KxX6OgTgfHCxL7yKVcmaynu8FRh23UFll1xIDqIDA==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-08-02T06:13:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jaWsLxPHTXzkWCyL+4O8UGDumAue8jO0l5tIkPLYt+rhttH5hljazzu+Dg8iVkzMizcDrrEhBn1c9frecuEnBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T18:43:36.828809Z"},"content_sha256":"bb5ca36445f8941783cfe228662db8abf655a233d290faff90602c1b96d7dabd","schema_version":"1.0","event_id":"sha256:bb5ca36445f8941783cfe228662db8abf655a233d290faff90602c1b96d7dabd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZLF6NH2N4AGTMDGFOPSZ3AX45K/bundle.json","state_url":"https://pith.science/pith/ZLF6NH2N4AGTMDGFOPSZ3AX45K/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZLF6NH2N4AGTMDGFOPSZ3AX45K/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-04T18:43:36Z","links":{"resolver":"https://pith.science/pith/ZLF6NH2N4AGTMDGFOPSZ3AX45K","bundle":"https://pith.science/pith/ZLF6NH2N4AGTMDGFOPSZ3AX45K/bundle.json","state":"https://pith.science/pith/ZLF6NH2N4AGTMDGFOPSZ3AX45K/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZLF6NH2N4AGTMDGFOPSZ3AX45K/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:ZLF6NH2N4AGTMDGFOPSZ3AX45K","merge_version":"pith-open-graph-merge-v1","event_count":3,"valid_event_count":3,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"c8b19cff6e47197e7d288ee71e084c40338c714de78cbe24d249eb65b0217594","cross_cats_sorted":["cs.CE","cs.RO"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.comp-ph","submitted_at":"2026-07-14T18:23:26Z","title_canon_sha256":"e67b5a3b58a236f2f61ed5aa10bee234e7352e35e9f1a718a3debc9a3df80dc9"},"schema_version":"1.0","source":{"id":"2607.13174","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.13174","created_at":"2026-07-16T00:22:01Z"},{"alias_kind":"arxiv_version","alias_value":"2607.13174v1","created_at":"2026-07-16T00:22:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.13174","created_at":"2026-07-16T00:22:01Z"},{"alias_kind":"pith_short_12","alias_value":"ZLF6NH2N4AGT","created_at":"2026-07-16T00:22:01Z"},{"alias_kind":"pith_short_16","alias_value":"ZLF6NH2N4AGTMDGF","created_at":"2026-07-16T00:22:01Z"},{"alias_kind":"pith_short_8","alias_value":"ZLF6NH2N","created_at":"2026-07-16T00:22:01Z"}],"graph_snapshots":[{"event_id":"sha256:1c7748bdb6c5dbcd66677e82c61f23644d90faab2cf6b61068938bd9e019907b","target":"graph","created_at":"2026-07-16T00:22:01Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2607.13174/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multimaterial 3D printing enables the fabrication of functionally graded components, but optimizing their spatial material distribution alongside structural topology remains a formidable challenge due to high-dimensional design spaces and complex constitutive modeling. This paper presents an end-to-end computational framework integrating sparsified physics-augmented neural networks with finite-element-based topology optimization. By extracting closed-form, composition-aware hyperelastic constitutive laws from experimental data, this approach facilitates exact symbolic differentiation via the a","authors_text":"Jingye Tan, Nikolaos Bouklas, Noy Cohen, Robert F. Shepherd, Steven Yang, Xue-Ling Luo","cross_cats":["cs.CE","cs.RO"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.comp-ph","submitted_at":"2026-07-14T18:23:26Z","title":"Towards end-to-end optimization in multimaterial 3D printing"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.13174","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:e7d15cf52f4e9a3c980f69699e1763d1b28df01034c665a346357a58705f5ac8","target":"record","created_at":"2026-07-16T00:22:01Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"c8b19cff6e47197e7d288ee71e084c40338c714de78cbe24d249eb65b0217594","cross_cats_sorted":["cs.CE","cs.RO"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.comp-ph","submitted_at":"2026-07-14T18:23:26Z","title_canon_sha256":"e67b5a3b58a236f2f61ed5aa10bee234e7352e35e9f1a718a3debc9a3df80dc9"},"schema_version":"1.0","source":{"id":"2607.13174","kind":"arxiv","version":1}},"canonical_sha256":"cacbe69f4de00d360cc573e59d82fceab8f776e9557f967c9b934e262e10df1e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cacbe69f4de00d360cc573e59d82fceab8f776e9557f967c9b934e262e10df1e","first_computed_at":"2026-07-16T00:22:01.182519Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-16T00:22:01.182519Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Y1mxomWf1/aThrelg9cwKl/+rr600PE6qOHmDP4RNF8CNSKNMEUwghXVNf+yYfg6gbgjOUKXEjA6xke+RIIGAg==","signature_status":"signed_v1","signed_at":"2026-07-16T00:22:01.183494Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.13174","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e7d15cf52f4e9a3c980f69699e1763d1b28df01034c665a346357a58705f5ac8","sha256:1c7748bdb6c5dbcd66677e82c61f23644d90faab2cf6b61068938bd9e019907b","sha256:bb5ca36445f8941783cfe228662db8abf655a233d290faff90602c1b96d7dabd"],"state_sha256":"69e93b17da51de75711988792d109d509efbf1bd41cca930b0e8bdd39cbd352d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"H2MJw/TK49HhI4iKmPOajNTsNgtBHuIbVYa/93+e8BrXMHll7P2yMUMxJGXgasBOSuoajteVhsMwgiD3zfa5DQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T18:43:36.834738Z","bundle_sha256":"9722ae90d952aca2d2103f31d609b2de9447c7736eec58a1e4ed975916ca6b79"}}