{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:LJFWY6X6IJHHJA5Q4VIWLF4Z4R","short_pith_number":"pith:LJFWY6X6","canonical_record":{"source":{"id":"2308.04967","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2023-08-09T14:08:01Z","cross_cats_sorted":["cs.NA"],"title_canon_sha256":"a6370d2a4397edd2dcbee2ae0ccfc30130a3f8cec8b9b868e73b8ed2f7dbdded","abstract_canon_sha256":"e44619f2b56e87828ae48af3dce498d6be7e429ad08acfffe23d182826f29f2f"},"schema_version":"1.0"},"canonical_sha256":"5a4b6c7afe424e7483b0e551659799e469c8394d7fc64ea31733df39472eb7da","source":{"kind":"arxiv","id":"2308.04967","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2308.04967","created_at":"2026-07-05T08:43:22Z"},{"alias_kind":"arxiv_version","alias_value":"2308.04967v2","created_at":"2026-07-05T08:43:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.04967","created_at":"2026-07-05T08:43:22Z"},{"alias_kind":"pith_short_12","alias_value":"LJFWY6X6IJHH","created_at":"2026-07-05T08:43:22Z"},{"alias_kind":"pith_short_16","alias_value":"LJFWY6X6IJHHJA5Q","created_at":"2026-07-05T08:43:22Z"},{"alias_kind":"pith_short_8","alias_value":"LJFWY6X6","created_at":"2026-07-05T08:43:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:LJFWY6X6IJHHJA5Q4VIWLF4Z4R","target":"record","payload":{"canonical_record":{"source":{"id":"2308.04967","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2023-08-09T14:08:01Z","cross_cats_sorted":["cs.NA"],"title_canon_sha256":"a6370d2a4397edd2dcbee2ae0ccfc30130a3f8cec8b9b868e73b8ed2f7dbdded","abstract_canon_sha256":"e44619f2b56e87828ae48af3dce498d6be7e429ad08acfffe23d182826f29f2f"},"schema_version":"1.0"},"canonical_sha256":"5a4b6c7afe424e7483b0e551659799e469c8394d7fc64ea31733df39472eb7da","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:43:22.902110Z","signature_b64":"0bZyqelYJVnqWQwgPTNeedNDlt/fLkbvVuPsXr5vr3FowpkKic0s63mpLnW+bRcaO4OiSiISsGcd1UhFxHu3Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5a4b6c7afe424e7483b0e551659799e469c8394d7fc64ea31733df39472eb7da","last_reissued_at":"2026-07-05T08:43:22.901695Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:43:22.901695Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2308.04967","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:43:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NsBqINMx64LPcmEGmoQFxfyVCsknpZrwdEnp5Zr6KoEAwY5disiBNDRvnxduGncDcQSnkuVi3PSup3q+HbEiDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T14:55:23.577891Z"},"content_sha256":"c11fb67ec2829b737c6bd007722885e1b0f363806718222f1254c8622d2bc6fd","schema_version":"1.0","event_id":"sha256:c11fb67ec2829b737c6bd007722885e1b0f363806718222f1254c8622d2bc6fd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:LJFWY6X6IJHHJA5Q4VIWLF4Z4R","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A pre-training deep learning method for simulating the large bending deformation of bilayer plates","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA"],"primary_cat":"math.NA","authors_text":"Pingbing Ming, Xiang Li, Yulei Liao","submitted_at":"2023-08-09T14:08:01Z","abstract_excerpt":"We propose a deep learning based method for simulating the large bending deformation of bilayer plates. Inspired by the greedy algorithm, we propose a pre-training method on a series of nested domains, which accelerate the convergence of training and find the absolute minimizer more effectively. The proposed method exhibits the capability to converge to an absolute minimizer, overcoming the limitation of gradient flow methods getting trapped in the local minimizer basins. We showcase better performance with fewer numbers of degrees of freedom for the relative energy errors and relative $L^2$-e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.04967","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/2308.04967/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":"2026-07-05T08:43:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CHOZhTo8mEqR1Qs8d6RG8CP1cSshu0bndGriPwNL8U6y3oom+IyYnmP3Ab7BDK6JrSlCSNDNsam1OF/wD2XgCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T14:55:23.578479Z"},"content_sha256":"5e13259b8847bf8056fba343b7a6724c32572d74297a1a9bec26d6d64dc3944f","schema_version":"1.0","event_id":"sha256:5e13259b8847bf8056fba343b7a6724c32572d74297a1a9bec26d6d64dc3944f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LJFWY6X6IJHHJA5Q4VIWLF4Z4R/bundle.json","state_url":"https://pith.science/pith/LJFWY6X6IJHHJA5Q4VIWLF4Z4R/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LJFWY6X6IJHHJA5Q4VIWLF4Z4R/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":"2026-08-19T14:55:23Z","links":{"resolver":"https://pith.science/pith/LJFWY6X6IJHHJA5Q4VIWLF4Z4R","bundle":"https://pith.science/pith/LJFWY6X6IJHHJA5Q4VIWLF4Z4R/bundle.json","state":"https://pith.science/pith/LJFWY6X6IJHHJA5Q4VIWLF4Z4R/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LJFWY6X6IJHHJA5Q4VIWLF4Z4R/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:LJFWY6X6IJHHJA5Q4VIWLF4Z4R","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":"e44619f2b56e87828ae48af3dce498d6be7e429ad08acfffe23d182826f29f2f","cross_cats_sorted":["cs.NA"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2023-08-09T14:08:01Z","title_canon_sha256":"a6370d2a4397edd2dcbee2ae0ccfc30130a3f8cec8b9b868e73b8ed2f7dbdded"},"schema_version":"1.0","source":{"id":"2308.04967","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2308.04967","created_at":"2026-07-05T08:43:22Z"},{"alias_kind":"arxiv_version","alias_value":"2308.04967v2","created_at":"2026-07-05T08:43:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.04967","created_at":"2026-07-05T08:43:22Z"},{"alias_kind":"pith_short_12","alias_value":"LJFWY6X6IJHH","created_at":"2026-07-05T08:43:22Z"},{"alias_kind":"pith_short_16","alias_value":"LJFWY6X6IJHHJA5Q","created_at":"2026-07-05T08:43:22Z"},{"alias_kind":"pith_short_8","alias_value":"LJFWY6X6","created_at":"2026-07-05T08:43:22Z"}],"graph_snapshots":[{"event_id":"sha256:5e13259b8847bf8056fba343b7a6724c32572d74297a1a9bec26d6d64dc3944f","target":"graph","created_at":"2026-07-05T08:43:22Z","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/2308.04967/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We propose a deep learning based method for simulating the large bending deformation of bilayer plates. Inspired by the greedy algorithm, we propose a pre-training method on a series of nested domains, which accelerate the convergence of training and find the absolute minimizer more effectively. The proposed method exhibits the capability to converge to an absolute minimizer, overcoming the limitation of gradient flow methods getting trapped in the local minimizer basins. We showcase better performance with fewer numbers of degrees of freedom for the relative energy errors and relative $L^2$-e","authors_text":"Pingbing Ming, Xiang Li, Yulei Liao","cross_cats":["cs.NA"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2023-08-09T14:08:01Z","title":"A pre-training deep learning method for simulating the large bending deformation of bilayer plates"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.04967","kind":"arxiv","version":2},"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:c11fb67ec2829b737c6bd007722885e1b0f363806718222f1254c8622d2bc6fd","target":"record","created_at":"2026-07-05T08:43:22Z","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":"e44619f2b56e87828ae48af3dce498d6be7e429ad08acfffe23d182826f29f2f","cross_cats_sorted":["cs.NA"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2023-08-09T14:08:01Z","title_canon_sha256":"a6370d2a4397edd2dcbee2ae0ccfc30130a3f8cec8b9b868e73b8ed2f7dbdded"},"schema_version":"1.0","source":{"id":"2308.04967","kind":"arxiv","version":2}},"canonical_sha256":"5a4b6c7afe424e7483b0e551659799e469c8394d7fc64ea31733df39472eb7da","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5a4b6c7afe424e7483b0e551659799e469c8394d7fc64ea31733df39472eb7da","first_computed_at":"2026-07-05T08:43:22.901695Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:43:22.901695Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0bZyqelYJVnqWQwgPTNeedNDlt/fLkbvVuPsXr5vr3FowpkKic0s63mpLnW+bRcaO4OiSiISsGcd1UhFxHu3Dg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:43:22.902110Z","signed_message":"canonical_sha256_bytes"},"source_id":"2308.04967","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c11fb67ec2829b737c6bd007722885e1b0f363806718222f1254c8622d2bc6fd","sha256:5e13259b8847bf8056fba343b7a6724c32572d74297a1a9bec26d6d64dc3944f"],"state_sha256":"188e7c46e8c88b4995648d40630d0cfbbbf0871dbbae6a16662d1be60b289d9a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"saQBfit0/axhkTaXaC/sCkWyQf2JzwqunzBqX03ngOr7HVfkFeFgiZCzH+uOgo3mBcICYos3frkjZpxAhqWdDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-19T14:55:23.583882Z","bundle_sha256":"3112c8d096fa89fb88eb6de95d6f23c890d72b1ec2e2a3309443ff6ab7d6b2e1"}}