{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:UJ77Z4YBNDYMZXGGUN334E3AHK","short_pith_number":"pith:UJ77Z4YB","canonical_record":{"source":{"id":"2302.00557","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-02-01T16:23:29Z","cross_cats_sorted":[],"title_canon_sha256":"0e9bbc9db309a028bad5bcae7dd94a4af98f2c8aaa4374a6650ed68b574c1bed","abstract_canon_sha256":"da4e945ba71c9b02d835d8540be163c5326014f207ffda3fccb0029273a55471"},"schema_version":"1.0"},"canonical_sha256":"a27ffcf30168f0ccdcc6a377be13603aa1c2c892ca23e983dfc36c9cd488fbba","source":{"kind":"arxiv","id":"2302.00557","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.00557","created_at":"2026-07-05T05:38:04Z"},{"alias_kind":"arxiv_version","alias_value":"2302.00557v1","created_at":"2026-07-05T05:38:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.00557","created_at":"2026-07-05T05:38:04Z"},{"alias_kind":"pith_short_12","alias_value":"UJ77Z4YBNDYM","created_at":"2026-07-05T05:38:04Z"},{"alias_kind":"pith_short_16","alias_value":"UJ77Z4YBNDYMZXGG","created_at":"2026-07-05T05:38:04Z"},{"alias_kind":"pith_short_8","alias_value":"UJ77Z4YB","created_at":"2026-07-05T05:38:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:UJ77Z4YBNDYMZXGGUN334E3AHK","target":"record","payload":{"canonical_record":{"source":{"id":"2302.00557","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-02-01T16:23:29Z","cross_cats_sorted":[],"title_canon_sha256":"0e9bbc9db309a028bad5bcae7dd94a4af98f2c8aaa4374a6650ed68b574c1bed","abstract_canon_sha256":"da4e945ba71c9b02d835d8540be163c5326014f207ffda3fccb0029273a55471"},"schema_version":"1.0"},"canonical_sha256":"a27ffcf30168f0ccdcc6a377be13603aa1c2c892ca23e983dfc36c9cd488fbba","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:38:04.592482Z","signature_b64":"Z7k8g76scTBqneDK/xKsNQlDIHXVLbpldTPOYW2vTz7KrnDSEnzNkTWZ/Mlk+YQZzbfuuW4zxYY2CFl1E5dmBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a27ffcf30168f0ccdcc6a377be13603aa1c2c892ca23e983dfc36c9cd488fbba","last_reissued_at":"2026-07-05T05:38:04.591983Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:38:04.591983Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2302.00557","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":"2026-07-05T05:38:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hN6ATUHdoHYuEK6Wd1H9c1Sun2/Wvwwjd9YW/aQcOAbgJ4RLrsAiFQFCpF333oUSg5ZTSoH3bsoBwZV6q/TODw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T15:45:11.202934Z"},"content_sha256":"5b0c27aef621b79e691869212600d3f44215c3644cfe78f286daf83935c63122","schema_version":"1.0","event_id":"sha256:5b0c27aef621b79e691869212600d3f44215c3644cfe78f286daf83935c63122"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:UJ77Z4YBNDYMZXGGUN334E3AHK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Graph Neural Network Based Surrogate Model of Physics Simulations for Geometry Design","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Chin Chun Ooi, David William Rosen, Guoying Dong, Jian Cheng Wong, Joyjit Chattoraj, Lucas Lestandi, Mark Hyunpong Jhon, My Ha Dao, Umesh Kizhakkinan","submitted_at":"2023-02-01T16:23:29Z","abstract_excerpt":"Computational Intelligence (CI) techniques have shown great potential as a surrogate model of expensive physics simulation, with demonstrated ability to make fast predictions, albeit at the expense of accuracy in some cases. For many scientific and engineering problems involving geometrical design, it is desirable for the surrogate models to precisely describe the change in geometry and predict the consequences. In that context, we develop graph neural networks (GNNs) as fast surrogate models for physics simulation, which allow us to directly train the models on 2/3D geometry designs that are "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.00557","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/2302.00557/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":"2026-07-05T05:38:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WfLIII2qD03W9jNlMJZ9hw6aumZqML2xLP31CIQLGfjYQfCiR5rYTfln79U46lpThBAk7XJHQNXWcBt6tKefBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T15:45:11.203816Z"},"content_sha256":"2b94334a5d557c242e3baf89a0bf10acbcadc9f8c8455cf0e496a144303c4629","schema_version":"1.0","event_id":"sha256:2b94334a5d557c242e3baf89a0bf10acbcadc9f8c8455cf0e496a144303c4629"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UJ77Z4YBNDYMZXGGUN334E3AHK/bundle.json","state_url":"https://pith.science/pith/UJ77Z4YBNDYMZXGGUN334E3AHK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UJ77Z4YBNDYMZXGGUN334E3AHK/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":"2026-08-21T15:45:11Z","links":{"resolver":"https://pith.science/pith/UJ77Z4YBNDYMZXGGUN334E3AHK","bundle":"https://pith.science/pith/UJ77Z4YBNDYMZXGGUN334E3AHK/bundle.json","state":"https://pith.science/pith/UJ77Z4YBNDYMZXGGUN334E3AHK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UJ77Z4YBNDYMZXGGUN334E3AHK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:UJ77Z4YBNDYMZXGGUN334E3AHK","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"da4e945ba71c9b02d835d8540be163c5326014f207ffda3fccb0029273a55471","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-02-01T16:23:29Z","title_canon_sha256":"0e9bbc9db309a028bad5bcae7dd94a4af98f2c8aaa4374a6650ed68b574c1bed"},"schema_version":"1.0","source":{"id":"2302.00557","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.00557","created_at":"2026-07-05T05:38:04Z"},{"alias_kind":"arxiv_version","alias_value":"2302.00557v1","created_at":"2026-07-05T05:38:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.00557","created_at":"2026-07-05T05:38:04Z"},{"alias_kind":"pith_short_12","alias_value":"UJ77Z4YBNDYM","created_at":"2026-07-05T05:38:04Z"},{"alias_kind":"pith_short_16","alias_value":"UJ77Z4YBNDYMZXGG","created_at":"2026-07-05T05:38:04Z"},{"alias_kind":"pith_short_8","alias_value":"UJ77Z4YB","created_at":"2026-07-05T05:38:04Z"}],"graph_snapshots":[{"event_id":"sha256:2b94334a5d557c242e3baf89a0bf10acbcadc9f8c8455cf0e496a144303c4629","target":"graph","created_at":"2026-07-05T05:38:04Z","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/2302.00557/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Computational Intelligence (CI) techniques have shown great potential as a surrogate model of expensive physics simulation, with demonstrated ability to make fast predictions, albeit at the expense of accuracy in some cases. For many scientific and engineering problems involving geometrical design, it is desirable for the surrogate models to precisely describe the change in geometry and predict the consequences. In that context, we develop graph neural networks (GNNs) as fast surrogate models for physics simulation, which allow us to directly train the models on 2/3D geometry designs that are ","authors_text":"Chin Chun Ooi, David William Rosen, Guoying Dong, Jian Cheng Wong, Joyjit Chattoraj, Lucas Lestandi, Mark Hyunpong Jhon, My Ha Dao, Umesh Kizhakkinan","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-02-01T16:23:29Z","title":"Graph Neural Network Based Surrogate Model of Physics Simulations for Geometry Design"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.00557","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":"sha256:5b0c27aef621b79e691869212600d3f44215c3644cfe78f286daf83935c63122","target":"record","created_at":"2026-07-05T05:38:04Z","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":"da4e945ba71c9b02d835d8540be163c5326014f207ffda3fccb0029273a55471","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-02-01T16:23:29Z","title_canon_sha256":"0e9bbc9db309a028bad5bcae7dd94a4af98f2c8aaa4374a6650ed68b574c1bed"},"schema_version":"1.0","source":{"id":"2302.00557","kind":"arxiv","version":1}},"canonical_sha256":"a27ffcf30168f0ccdcc6a377be13603aa1c2c892ca23e983dfc36c9cd488fbba","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a27ffcf30168f0ccdcc6a377be13603aa1c2c892ca23e983dfc36c9cd488fbba","first_computed_at":"2026-07-05T05:38:04.591983Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:38:04.591983Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Z7k8g76scTBqneDK/xKsNQlDIHXVLbpldTPOYW2vTz7KrnDSEnzNkTWZ/Mlk+YQZzbfuuW4zxYY2CFl1E5dmBA==","signature_status":"signed_v1","signed_at":"2026-07-05T05:38:04.592482Z","signed_message":"canonical_sha256_bytes"},"source_id":"2302.00557","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5b0c27aef621b79e691869212600d3f44215c3644cfe78f286daf83935c63122","sha256:2b94334a5d557c242e3baf89a0bf10acbcadc9f8c8455cf0e496a144303c4629"],"state_sha256":"ca501283d7180d6e2ab6930f8bca91fe487957f89483a847cca1b520ceff9fb2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CNppcL74wEWqD6DDGUr2ZBv6fNQt2fr1Uu9CMOZhOkVrnQkRtLn2G2XBqWM23aZX/4XmtxhUM7lwlQBMuXvUBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-21T15:45:11.213946Z","bundle_sha256":"e1ed5b6caa6862d000f92edb61e958dcfc0579437fc9b363c82acb5dfba2b836"}}