{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:TX34BND5AHWDOOK3FN4BIHEZYG","short_pith_number":"pith:TX34BND5","schema_version":"1.0","canonical_sha256":"9df7c0b47d01ec37395b2b78141c99c1ab2390a7886f71d7f4cd844a4022b772","source":{"kind":"arxiv","id":"2412.05576","version":2},"attestation_state":"computed","paper":{"title":"STONet: A neural operator for modeling solute transport in micro-cracked reservoirs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CE","cs.NE","physics.flu-dyn"],"primary_cat":"cs.LG","authors_text":"Ehsan Haghighat, Mohammad Hesan Adeli, Ruben Juanes, S Mohammad Mousavi","submitted_at":"2024-12-07T07:53:47Z","abstract_excerpt":"In this work, we introduce a novel neural operator, the Solute Transport Operator Network (STONet), to efficiently model contaminant transport in micro-cracked porous media. STONet's model architecture is specifically designed for this problem and uniquely integrates an enriched DeepONet structure with a transformer-based multi-head attention mechanism, enhancing performance without incurring additional computational overhead compared to existing neural operators. The model combines different networks to encode heterogeneous properties effectively and predict the rate of change of the concentr"},"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":"2412.05576","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-12-07T07:53:47Z","cross_cats_sorted":["cs.CE","cs.NE","physics.flu-dyn"],"title_canon_sha256":"20e51f3e85f2ff1e9d78f1c7fb3cc058b97025ae504ecfd39483c2b5ce409700","abstract_canon_sha256":"6c051b0e57718cb7a87994dbef48dcf32e56698c7e1bf4dd6563e00083c2e3b2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:29:52.908121Z","signature_b64":"1uTrHBsA9yMCoG+ALznzCjZ1YsJOmZV2OLDEEjaZ2Znr/gTH03uhJrWOLsRYwunPGQm/oMtQeT1dLDU0f4u4DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9df7c0b47d01ec37395b2b78141c99c1ab2390a7886f71d7f4cd844a4022b772","last_reissued_at":"2026-07-05T11:29:52.907637Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:29:52.907637Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"STONet: A neural operator for modeling solute transport in micro-cracked reservoirs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CE","cs.NE","physics.flu-dyn"],"primary_cat":"cs.LG","authors_text":"Ehsan Haghighat, Mohammad Hesan Adeli, Ruben Juanes, S Mohammad Mousavi","submitted_at":"2024-12-07T07:53:47Z","abstract_excerpt":"In this work, we introduce a novel neural operator, the Solute Transport Operator Network (STONet), to efficiently model contaminant transport in micro-cracked porous media. STONet's model architecture is specifically designed for this problem and uniquely integrates an enriched DeepONet structure with a transformer-based multi-head attention mechanism, enhancing performance without incurring additional computational overhead compared to existing neural operators. The model combines different networks to encode heterogeneous properties effectively and predict the rate of change of the concentr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.05576","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/2412.05576/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":"2412.05576","created_at":"2026-07-05T11:29:52.907696+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.05576v2","created_at":"2026-07-05T11:29:52.907696+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.05576","created_at":"2026-07-05T11:29:52.907696+00:00"},{"alias_kind":"pith_short_12","alias_value":"TX34BND5AHWD","created_at":"2026-07-05T11:29:52.907696+00:00"},{"alias_kind":"pith_short_16","alias_value":"TX34BND5AHWDOOK3","created_at":"2026-07-05T11:29:52.907696+00:00"},{"alias_kind":"pith_short_8","alias_value":"TX34BND5","created_at":"2026-07-05T11:29:52.907696+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.05576","citing_title":"STONet: A neural operator for modeling solute transport in micro-cracked reservoirs","ref_index":1,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TX34BND5AHWDOOK3FN4BIHEZYG","json":"https://pith.science/pith/TX34BND5AHWDOOK3FN4BIHEZYG.json","graph_json":"https://pith.science/api/pith-number/TX34BND5AHWDOOK3FN4BIHEZYG/graph.json","events_json":"https://pith.science/api/pith-number/TX34BND5AHWDOOK3FN4BIHEZYG/events.json","paper":"https://pith.science/paper/TX34BND5"},"agent_actions":{"view_html":"https://pith.science/pith/TX34BND5AHWDOOK3FN4BIHEZYG","download_json":"https://pith.science/pith/TX34BND5AHWDOOK3FN4BIHEZYG.json","view_paper":"https://pith.science/paper/TX34BND5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.05576&json=true","fetch_graph":"https://pith.science/api/pith-number/TX34BND5AHWDOOK3FN4BIHEZYG/graph.json","fetch_events":"https://pith.science/api/pith-number/TX34BND5AHWDOOK3FN4BIHEZYG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TX34BND5AHWDOOK3FN4BIHEZYG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TX34BND5AHWDOOK3FN4BIHEZYG/action/storage_attestation","attest_author":"https://pith.science/pith/TX34BND5AHWDOOK3FN4BIHEZYG/action/author_attestation","sign_citation":"https://pith.science/pith/TX34BND5AHWDOOK3FN4BIHEZYG/action/citation_signature","submit_replication":"https://pith.science/pith/TX34BND5AHWDOOK3FN4BIHEZYG/action/replication_record"}},"created_at":"2026-07-05T11:29:52.907696+00:00","updated_at":"2026-07-05T11:29:52.907696+00:00"}