{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:LNDOKYV4LE5EROM3TPBL2DG2YM","short_pith_number":"pith:LNDOKYV4","canonical_record":{"source":{"id":"1806.06094","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-06-15T18:58:16Z","cross_cats_sorted":[],"title_canon_sha256":"86b114ff7ccbe01e5d54ce0f1a4ac1dcb80c856a7080a00247a79704d458601a","abstract_canon_sha256":"5aa6659d61194d7e1e0f992ace7f0cf518b020feeaf76a6f266750f10b53f2f0"},"schema_version":"1.0"},"canonical_sha256":"5b46e562bc593a48b99b9bc2bd0cdac32a9b9d8475c6e319e8f30c6794a5dee1","source":{"kind":"arxiv","id":"1806.06094","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.06094","created_at":"2026-05-18T00:04:44Z"},{"alias_kind":"arxiv_version","alias_value":"1806.06094v2","created_at":"2026-05-18T00:04:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.06094","created_at":"2026-05-18T00:04:44Z"},{"alias_kind":"pith_short_12","alias_value":"LNDOKYV4LE5E","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_16","alias_value":"LNDOKYV4LE5EROM3","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_8","alias_value":"LNDOKYV4","created_at":"2026-05-18T12:32:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:LNDOKYV4LE5EROM3TPBL2DG2YM","target":"record","payload":{"canonical_record":{"source":{"id":"1806.06094","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-06-15T18:58:16Z","cross_cats_sorted":[],"title_canon_sha256":"86b114ff7ccbe01e5d54ce0f1a4ac1dcb80c856a7080a00247a79704d458601a","abstract_canon_sha256":"5aa6659d61194d7e1e0f992ace7f0cf518b020feeaf76a6f266750f10b53f2f0"},"schema_version":"1.0"},"canonical_sha256":"5b46e562bc593a48b99b9bc2bd0cdac32a9b9d8475c6e319e8f30c6794a5dee1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:04:44.682010Z","signature_b64":"F+AH3XFW9pzaXnZ9eSAaFInR9fOOJDyOpKpyOjnd5ghP6WeBdLKwNxH76WucfpZTQJQYy7OjfEQH9AAyRdAJAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5b46e562bc593a48b99b9bc2bd0cdac32a9b9d8475c6e319e8f30c6794a5dee1","last_reissued_at":"2026-05-18T00:04:44.681475Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:04:44.681475Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1806.06094","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":"2026-05-18T00:04:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4dh4dyslBrk77kyBHkSNyuRR4wVlD9jqpisGwzuJ6CgyBh4VPXmZG16IVFgXRSZoCoYs1xYyTmuTJs855K5FCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-12T00:56:52.669361Z"},"content_sha256":"78834f84b8563456279b2802e03e8ffa6bf7e315b7561d175600721a7c9a9daa","schema_version":"1.0","event_id":"sha256:78834f84b8563456279b2802e03e8ffa6bf7e315b7561d175600721a7c9a9daa"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:LNDOKYV4LE5EROM3TPBL2DG2YM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SPNets: Differentiable Fluid Dynamics for Deep Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Connor Schenck, Dieter Fox","submitted_at":"2018-06-15T18:58:16Z","abstract_excerpt":"In this paper we introduce Smooth Particle Networks (SPNets), a framework for integrating fluid dynamics with deep networks. SPNets adds two new layers to the neural network toolbox: ConvSP and ConvSDF, which enable computing physical interactions with unordered particle sets. We use these lay- ers in combination with standard neural network layers to directly implement fluid dynamics inside a deep network, where the parameters of the network are the fluid parameters themselves (e.g., viscosity, cohesion, etc.). Because SPNets are imple- mented as a neural network, the resulting fluid dynamics"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.06094","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":""},"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":"2026-05-18T00:04:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"j4XWfLGjfEHEfQgps4pfLBD0++G/lmckjHd3UChJguEwGmbdToehbMbajupPGLszpbeZWLcTLZYehj9jOf0rDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-12T00:56:52.670096Z"},"content_sha256":"fa15a14c053e68f33a1346d95d63a3f8c856e7f89c8f4e55704302238b7d3342","schema_version":"1.0","event_id":"sha256:fa15a14c053e68f33a1346d95d63a3f8c856e7f89c8f4e55704302238b7d3342"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LNDOKYV4LE5EROM3TPBL2DG2YM/bundle.json","state_url":"https://pith.science/pith/LNDOKYV4LE5EROM3TPBL2DG2YM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LNDOKYV4LE5EROM3TPBL2DG2YM/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":"2026-06-12T00:56:52Z","links":{"resolver":"https://pith.science/pith/LNDOKYV4LE5EROM3TPBL2DG2YM","bundle":"https://pith.science/pith/LNDOKYV4LE5EROM3TPBL2DG2YM/bundle.json","state":"https://pith.science/pith/LNDOKYV4LE5EROM3TPBL2DG2YM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LNDOKYV4LE5EROM3TPBL2DG2YM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:LNDOKYV4LE5EROM3TPBL2DG2YM","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":"5aa6659d61194d7e1e0f992ace7f0cf518b020feeaf76a6f266750f10b53f2f0","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-06-15T18:58:16Z","title_canon_sha256":"86b114ff7ccbe01e5d54ce0f1a4ac1dcb80c856a7080a00247a79704d458601a"},"schema_version":"1.0","source":{"id":"1806.06094","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.06094","created_at":"2026-05-18T00:04:44Z"},{"alias_kind":"arxiv_version","alias_value":"1806.06094v2","created_at":"2026-05-18T00:04:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.06094","created_at":"2026-05-18T00:04:44Z"},{"alias_kind":"pith_short_12","alias_value":"LNDOKYV4LE5E","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_16","alias_value":"LNDOKYV4LE5EROM3","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_8","alias_value":"LNDOKYV4","created_at":"2026-05-18T12:32:37Z"}],"graph_snapshots":[{"event_id":"sha256:fa15a14c053e68f33a1346d95d63a3f8c856e7f89c8f4e55704302238b7d3342","target":"graph","created_at":"2026-05-18T00:04:44Z","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"},"paper":{"abstract_excerpt":"In this paper we introduce Smooth Particle Networks (SPNets), a framework for integrating fluid dynamics with deep networks. SPNets adds two new layers to the neural network toolbox: ConvSP and ConvSDF, which enable computing physical interactions with unordered particle sets. We use these lay- ers in combination with standard neural network layers to directly implement fluid dynamics inside a deep network, where the parameters of the network are the fluid parameters themselves (e.g., viscosity, cohesion, etc.). Because SPNets are imple- mented as a neural network, the resulting fluid dynamics","authors_text":"Connor Schenck, Dieter Fox","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-06-15T18:58:16Z","title":"SPNets: Differentiable Fluid Dynamics for Deep Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.06094","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":"sha256:78834f84b8563456279b2802e03e8ffa6bf7e315b7561d175600721a7c9a9daa","target":"record","created_at":"2026-05-18T00:04:44Z","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":"5aa6659d61194d7e1e0f992ace7f0cf518b020feeaf76a6f266750f10b53f2f0","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-06-15T18:58:16Z","title_canon_sha256":"86b114ff7ccbe01e5d54ce0f1a4ac1dcb80c856a7080a00247a79704d458601a"},"schema_version":"1.0","source":{"id":"1806.06094","kind":"arxiv","version":2}},"canonical_sha256":"5b46e562bc593a48b99b9bc2bd0cdac32a9b9d8475c6e319e8f30c6794a5dee1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5b46e562bc593a48b99b9bc2bd0cdac32a9b9d8475c6e319e8f30c6794a5dee1","first_computed_at":"2026-05-18T00:04:44.681475Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:04:44.681475Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"F+AH3XFW9pzaXnZ9eSAaFInR9fOOJDyOpKpyOjnd5ghP6WeBdLKwNxH76WucfpZTQJQYy7OjfEQH9AAyRdAJAg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:04:44.682010Z","signed_message":"canonical_sha256_bytes"},"source_id":"1806.06094","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:78834f84b8563456279b2802e03e8ffa6bf7e315b7561d175600721a7c9a9daa","sha256:fa15a14c053e68f33a1346d95d63a3f8c856e7f89c8f4e55704302238b7d3342"],"state_sha256":"5c2b9fe42489fd07a8a6489ce9efae21421142b3fdc68b9301ce414f0124239f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bkKCT9iEptvEeQHIrOEcZ8J5GTZ5jmglhDlul/2dZFUs7GasKmqW9rqRmTT6RSX2Wizrvm0keVPn0L0BY33oCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-12T00:56:52.674946Z","bundle_sha256":"d017bb97b8c44a80ed669db76165aefad6891bab3813dd0380e95d6d2be4912e"}}