{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:DNK4HXVBXGBDLPQWPVTU5V3RHB","short_pith_number":"pith:DNK4HXVB","schema_version":"1.0","canonical_sha256":"1b55c3dea1b98235be167d674ed7713859aa623a3980deb68b6ca87b97828797","source":{"kind":"arxiv","id":"2401.08886","version":2},"attestation_state":"computed","paper":{"title":"RiemannONets: Interpretable Neural Operators for Riemann Problems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["physics.flu-dyn"],"primary_cat":"cs.LG","authors_text":"Ahmad Peyvan, Ameya D. Jagtap, George Em Karniadakis, Vivek Oommen","submitted_at":"2024-01-16T23:45:14Z","abstract_excerpt":"Developing the proper representations for simulating high-speed flows with strong shock waves, rarefactions, and contact discontinuities has been a long-standing question in numerical analysis. Herein, we employ neural operators to solve Riemann problems encountered in compressible flows for extreme pressure jumps (up to $10^{10}$ pressure ratio). In particular, we first consider the DeepONet that we train in a two-stage process, following the recent work of \\cite{lee2023training}, wherein the first stage, a basis is extracted from the trunk net, which is orthonormalized and subsequently is us"},"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":"2401.08886","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-01-16T23:45:14Z","cross_cats_sorted":["physics.flu-dyn"],"title_canon_sha256":"8e0a8a9b80ab004ef4879345215ba63ed3cb067fe18af38330ef6fe8696a77a1","abstract_canon_sha256":"fde2ded071556fa6d7460717a0680422f53dc8b145533c5230a32ac3b70a9a84"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:10:22.081062Z","signature_b64":"OhUqXKFP/ljAa1r4noQ/GKr8OnmAzuM9dTBtP7EUHSPrrJWE4bturJ3dniMf+rKRZoeNKGXl2+MzR3FUAIOTAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1b55c3dea1b98235be167d674ed7713859aa623a3980deb68b6ca87b97828797","last_reissued_at":"2026-07-05T08:10:22.080574Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:10:22.080574Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RiemannONets: Interpretable Neural Operators for Riemann Problems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["physics.flu-dyn"],"primary_cat":"cs.LG","authors_text":"Ahmad Peyvan, Ameya D. Jagtap, George Em Karniadakis, Vivek Oommen","submitted_at":"2024-01-16T23:45:14Z","abstract_excerpt":"Developing the proper representations for simulating high-speed flows with strong shock waves, rarefactions, and contact discontinuities has been a long-standing question in numerical analysis. Herein, we employ neural operators to solve Riemann problems encountered in compressible flows for extreme pressure jumps (up to $10^{10}$ pressure ratio). In particular, we first consider the DeepONet that we train in a two-stage process, following the recent work of \\cite{lee2023training}, wherein the first stage, a basis is extracted from the trunk net, which is orthonormalized and subsequently is us"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.08886","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/2401.08886/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":"2401.08886","created_at":"2026-07-05T08:10:22.080632+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.08886v2","created_at":"2026-07-05T08:10:22.080632+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.08886","created_at":"2026-07-05T08:10:22.080632+00:00"},{"alias_kind":"pith_short_12","alias_value":"DNK4HXVBXGBD","created_at":"2026-07-05T08:10:22.080632+00:00"},{"alias_kind":"pith_short_16","alias_value":"DNK4HXVBXGBDLPQW","created_at":"2026-07-05T08:10:22.080632+00:00"},{"alias_kind":"pith_short_8","alias_value":"DNK4HXVB","created_at":"2026-07-05T08:10:22.080632+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2407.00809","citing_title":"Kernel Neural Operators (KNOs) for Scalable, Memory-efficient, Geometrically-flexible Operator Learning","ref_index":33,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DNK4HXVBXGBDLPQWPVTU5V3RHB","json":"https://pith.science/pith/DNK4HXVBXGBDLPQWPVTU5V3RHB.json","graph_json":"https://pith.science/api/pith-number/DNK4HXVBXGBDLPQWPVTU5V3RHB/graph.json","events_json":"https://pith.science/api/pith-number/DNK4HXVBXGBDLPQWPVTU5V3RHB/events.json","paper":"https://pith.science/paper/DNK4HXVB"},"agent_actions":{"view_html":"https://pith.science/pith/DNK4HXVBXGBDLPQWPVTU5V3RHB","download_json":"https://pith.science/pith/DNK4HXVBXGBDLPQWPVTU5V3RHB.json","view_paper":"https://pith.science/paper/DNK4HXVB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.08886&json=true","fetch_graph":"https://pith.science/api/pith-number/DNK4HXVBXGBDLPQWPVTU5V3RHB/graph.json","fetch_events":"https://pith.science/api/pith-number/DNK4HXVBXGBDLPQWPVTU5V3RHB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DNK4HXVBXGBDLPQWPVTU5V3RHB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DNK4HXVBXGBDLPQWPVTU5V3RHB/action/storage_attestation","attest_author":"https://pith.science/pith/DNK4HXVBXGBDLPQWPVTU5V3RHB/action/author_attestation","sign_citation":"https://pith.science/pith/DNK4HXVBXGBDLPQWPVTU5V3RHB/action/citation_signature","submit_replication":"https://pith.science/pith/DNK4HXVBXGBDLPQWPVTU5V3RHB/action/replication_record"}},"created_at":"2026-07-05T08:10:22.080632+00:00","updated_at":"2026-07-05T08:10:22.080632+00:00"}