{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:XE67GRPJQNVQJOYIG2N3BZ4CEX","short_pith_number":"pith:XE67GRPJ","schema_version":"1.0","canonical_sha256":"b93df345e9836b04bb08369bb0e78225dac6493ce3bd7e1df9bba2eee8e6be24","source":{"kind":"arxiv","id":"2404.14401","version":3},"attestation_state":"computed","paper":{"title":"A Python GPU-accelerated solver for the Gross-Pitaevskii equation and applications to many-body cavity QED","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cond-mat.quant-gas","physics.atom-ph","quant-ph"],"primary_cat":"physics.comp-ph","authors_text":"Alexander Baumg\\\"artner, Davide Dreon, Fabian Finger, Justyna Stefaniak, Lorenzo Fioroni, Luca Gravina, Tobias Donner","submitted_at":"2024-04-22T17:58:34Z","abstract_excerpt":"TorchGPE is a general-purpose Python package developed for solving the Gross-Pitaevskii equation (GPE). This solver is designed to integrate wave functions across a spectrum of linear and non-linear potentials. A distinctive aspect of TorchGPE is its modular approach, which allows the incorporation of arbitrary self-consistent and time-dependent potentials, e.g., those relevant in many-body cavity QED models. The package employs a symmetric split-step Fourier propagation method, effective in both real and imaginary time. In our work, we demonstrate a significant improvement in computational ef"},"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":"2404.14401","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.comp-ph","submitted_at":"2024-04-22T17:58:34Z","cross_cats_sorted":["cond-mat.quant-gas","physics.atom-ph","quant-ph"],"title_canon_sha256":"6e2500f940edfb47301c77b748ac9b6ee2cdd35aa6bbc5ef80a38fefa86bfe68","abstract_canon_sha256":"1c58ee82708df944005fdbde2b53476f1f0d46aa41e250b6a9923f15c7a860c8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:33:31.191645Z","signature_b64":"pJRVIwPfhsrH+rJyQLF6TRoshfxZKyu09ztdVS0+zzk0KJp8MgGvBCNqGoXnv3mq5nesSUvbq09v8/8zxFM0Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b93df345e9836b04bb08369bb0e78225dac6493ce3bd7e1df9bba2eee8e6be24","last_reissued_at":"2026-07-05T09:33:31.191182Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:33:31.191182Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Python GPU-accelerated solver for the Gross-Pitaevskii equation and applications to many-body cavity QED","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cond-mat.quant-gas","physics.atom-ph","quant-ph"],"primary_cat":"physics.comp-ph","authors_text":"Alexander Baumg\\\"artner, Davide Dreon, Fabian Finger, Justyna Stefaniak, Lorenzo Fioroni, Luca Gravina, Tobias Donner","submitted_at":"2024-04-22T17:58:34Z","abstract_excerpt":"TorchGPE is a general-purpose Python package developed for solving the Gross-Pitaevskii equation (GPE). This solver is designed to integrate wave functions across a spectrum of linear and non-linear potentials. A distinctive aspect of TorchGPE is its modular approach, which allows the incorporation of arbitrary self-consistent and time-dependent potentials, e.g., those relevant in many-body cavity QED models. The package employs a symmetric split-step Fourier propagation method, effective in both real and imaginary time. In our work, we demonstrate a significant improvement in computational ef"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.14401","kind":"arxiv","version":3},"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/2404.14401/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":"2404.14401","created_at":"2026-07-05T09:33:31.191240+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.14401v3","created_at":"2026-07-05T09:33:31.191240+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.14401","created_at":"2026-07-05T09:33:31.191240+00:00"},{"alias_kind":"pith_short_12","alias_value":"XE67GRPJQNVQ","created_at":"2026-07-05T09:33:31.191240+00:00"},{"alias_kind":"pith_short_16","alias_value":"XE67GRPJQNVQJOYI","created_at":"2026-07-05T09:33:31.191240+00:00"},{"alias_kind":"pith_short_8","alias_value":"XE67GRPJ","created_at":"2026-07-05T09:33:31.191240+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2507.04262","citing_title":"Solving the Gross-Pitaevskii equation on multiple different scales using the quantics tensor train representation","ref_index":84,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XE67GRPJQNVQJOYIG2N3BZ4CEX","json":"https://pith.science/pith/XE67GRPJQNVQJOYIG2N3BZ4CEX.json","graph_json":"https://pith.science/api/pith-number/XE67GRPJQNVQJOYIG2N3BZ4CEX/graph.json","events_json":"https://pith.science/api/pith-number/XE67GRPJQNVQJOYIG2N3BZ4CEX/events.json","paper":"https://pith.science/paper/XE67GRPJ"},"agent_actions":{"view_html":"https://pith.science/pith/XE67GRPJQNVQJOYIG2N3BZ4CEX","download_json":"https://pith.science/pith/XE67GRPJQNVQJOYIG2N3BZ4CEX.json","view_paper":"https://pith.science/paper/XE67GRPJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.14401&json=true","fetch_graph":"https://pith.science/api/pith-number/XE67GRPJQNVQJOYIG2N3BZ4CEX/graph.json","fetch_events":"https://pith.science/api/pith-number/XE67GRPJQNVQJOYIG2N3BZ4CEX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XE67GRPJQNVQJOYIG2N3BZ4CEX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XE67GRPJQNVQJOYIG2N3BZ4CEX/action/storage_attestation","attest_author":"https://pith.science/pith/XE67GRPJQNVQJOYIG2N3BZ4CEX/action/author_attestation","sign_citation":"https://pith.science/pith/XE67GRPJQNVQJOYIG2N3BZ4CEX/action/citation_signature","submit_replication":"https://pith.science/pith/XE67GRPJQNVQJOYIG2N3BZ4CEX/action/replication_record"}},"created_at":"2026-07-05T09:33:31.191240+00:00","updated_at":"2026-07-05T09:33:31.191240+00:00"}