{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:FSRJEZQI26BMELOWFIZPXVRHWL","short_pith_number":"pith:FSRJEZQI","schema_version":"1.0","canonical_sha256":"2ca2926608d782c22dd62a32fbd627b2ed0b81aa542854d9d5bc73605f14b627","source":{"kind":"arxiv","id":"2607.15049","version":1},"attestation_state":"computed","paper":{"title":"Neural operators solve inverse problems for constitutive model discovery","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.mtrl-sci"],"primary_cat":"cs.CE","authors_text":"Burigede Liu, Ellen Kuhl, Moritz Flaschel","submitted_at":"2026-07-16T14:29:38Z","abstract_excerpt":"Characterizing the mechanical response of materials traditionally requires solving optimization problems in which model parameters are calibrated or trained to minimize the discrepancy between model predictions and experimental data. This process can be computationally expensive and time-consuming. To overcome this limitation, we propose two neural operator architectures that directly map experimentally measured data to the constitutive functions governing the mechanical response of the material: Physics-Augmented Neural Operators (PANO) and Constitutive Artificial Neural Operators (CANO). 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":"2607.15049","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CE","submitted_at":"2026-07-16T14:29:38Z","cross_cats_sorted":["cond-mat.mtrl-sci"],"title_canon_sha256":"813f84f32c794010ff91dea13bae3f69ba806a95e507ab9de737672679f1f9d7","abstract_canon_sha256":"6e134b7d7152483f630d1ac68ad459ce9512dc1ab5c38f4ca1e01e22fc2f4070"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-17T01:22:06.941467Z","signature_b64":"KzCAytfK7dA6x6dhUKa7aBhuSz0idJmL71ad3WhypaG08OI8ZJdGm4nJPC5awBIsZz4s+c/crqsqOrnlvK0/AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2ca2926608d782c22dd62a32fbd627b2ed0b81aa542854d9d5bc73605f14b627","last_reissued_at":"2026-07-17T01:22:06.940619Z","signature_status":"signed_v1","first_computed_at":"2026-07-17T01:22:06.940619Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Neural operators solve inverse problems for constitutive model discovery","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.mtrl-sci"],"primary_cat":"cs.CE","authors_text":"Burigede Liu, Ellen Kuhl, Moritz Flaschel","submitted_at":"2026-07-16T14:29:38Z","abstract_excerpt":"Characterizing the mechanical response of materials traditionally requires solving optimization problems in which model parameters are calibrated or trained to minimize the discrepancy between model predictions and experimental data. This process can be computationally expensive and time-consuming. To overcome this limitation, we propose two neural operator architectures that directly map experimentally measured data to the constitutive functions governing the mechanical response of the material: Physics-Augmented Neural Operators (PANO) and Constitutive Artificial Neural Operators (CANO). The"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.15049","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/2607.15049/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":"2607.15049","created_at":"2026-07-17T01:22:06.941061+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.15049v1","created_at":"2026-07-17T01:22:06.941061+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.15049","created_at":"2026-07-17T01:22:06.941061+00:00"},{"alias_kind":"pith_short_12","alias_value":"FSRJEZQI26BM","created_at":"2026-07-17T01:22:06.941061+00:00"},{"alias_kind":"pith_short_16","alias_value":"FSRJEZQI26BMELOW","created_at":"2026-07-17T01:22:06.941061+00:00"},{"alias_kind":"pith_short_8","alias_value":"FSRJEZQI","created_at":"2026-07-17T01:22:06.941061+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FSRJEZQI26BMELOWFIZPXVRHWL","json":"https://pith.science/pith/FSRJEZQI26BMELOWFIZPXVRHWL.json","graph_json":"https://pith.science/api/pith-number/FSRJEZQI26BMELOWFIZPXVRHWL/graph.json","events_json":"https://pith.science/api/pith-number/FSRJEZQI26BMELOWFIZPXVRHWL/events.json","paper":"https://pith.science/paper/FSRJEZQI"},"agent_actions":{"view_html":"https://pith.science/pith/FSRJEZQI26BMELOWFIZPXVRHWL","download_json":"https://pith.science/pith/FSRJEZQI26BMELOWFIZPXVRHWL.json","view_paper":"https://pith.science/paper/FSRJEZQI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.15049&json=true","fetch_graph":"https://pith.science/api/pith-number/FSRJEZQI26BMELOWFIZPXVRHWL/graph.json","fetch_events":"https://pith.science/api/pith-number/FSRJEZQI26BMELOWFIZPXVRHWL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FSRJEZQI26BMELOWFIZPXVRHWL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FSRJEZQI26BMELOWFIZPXVRHWL/action/storage_attestation","attest_author":"https://pith.science/pith/FSRJEZQI26BMELOWFIZPXVRHWL/action/author_attestation","sign_citation":"https://pith.science/pith/FSRJEZQI26BMELOWFIZPXVRHWL/action/citation_signature","submit_replication":"https://pith.science/pith/FSRJEZQI26BMELOWFIZPXVRHWL/action/replication_record"}},"created_at":"2026-07-17T01:22:06.941061+00:00","updated_at":"2026-07-17T01:22:06.941061+00:00"}