{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:PLPXWC2ZZNILBF4GMARUHV5PTP","short_pith_number":"pith:PLPXWC2Z","canonical_record":{"source":{"id":"2505.19320","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-05-25T21:12:01Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"7746e4937ad3d16d075ed8ba2f52d133604cad7e49d2a8b5c5a870c92d82589a","abstract_canon_sha256":"55cb68e3a09d3ccf24f6b107ed52cbac7f0be75c1258ecc56b389f1753c275df"},"schema_version":"1.0"},"canonical_sha256":"7adf7b0b59cb50b09786602343d7af9bc3186120e02bc6e3106922b3948219ea","source":{"kind":"arxiv","id":"2505.19320","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.19320","created_at":"2026-07-05T11:09:28Z"},{"alias_kind":"arxiv_version","alias_value":"2505.19320v1","created_at":"2026-07-05T11:09:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.19320","created_at":"2026-07-05T11:09:28Z"},{"alias_kind":"pith_short_12","alias_value":"PLPXWC2ZZNIL","created_at":"2026-07-05T11:09:28Z"},{"alias_kind":"pith_short_16","alias_value":"PLPXWC2ZZNILBF4G","created_at":"2026-07-05T11:09:28Z"},{"alias_kind":"pith_short_8","alias_value":"PLPXWC2Z","created_at":"2026-07-05T11:09:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:PLPXWC2ZZNILBF4GMARUHV5PTP","target":"record","payload":{"canonical_record":{"source":{"id":"2505.19320","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-05-25T21:12:01Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"7746e4937ad3d16d075ed8ba2f52d133604cad7e49d2a8b5c5a870c92d82589a","abstract_canon_sha256":"55cb68e3a09d3ccf24f6b107ed52cbac7f0be75c1258ecc56b389f1753c275df"},"schema_version":"1.0"},"canonical_sha256":"7adf7b0b59cb50b09786602343d7af9bc3186120e02bc6e3106922b3948219ea","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:09:28.271162Z","signature_b64":"bYf0dfc37xi1Zi3Zx1dinZZ64EXJ3AkgdpOzYFPsVvz2niy2TBe2GwNjpVjZHkRUprh3Ri5UTaDYlVUTIY/vAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7adf7b0b59cb50b09786602343d7af9bc3186120e02bc6e3106922b3948219ea","last_reissued_at":"2026-07-05T11:09:28.270482Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:09:28.270482Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.19320","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:09:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YolQ7jGzf7zPY5zx5QYeShVNqviyxUdS+mp9/0M0QkNtYMfTZXph/+5MG5SrxK0Ehj6VHu+7N0FFk5n8cEBzBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T15:16:21.861384Z"},"content_sha256":"4e7332c7e472864129eefb288d2b126682a1167e5692b7f63061b67f390eb1d8","schema_version":"1.0","event_id":"sha256:4e7332c7e472864129eefb288d2b126682a1167e5692b7f63061b67f390eb1d8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:PLPXWC2ZZNILBF4GMARUHV5PTP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"PIGPVAE: Physics-Informed Gaussian Process Variational Autoencoders","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Abdulmajid Murad, Alessandro Nocente, Massimiliano Ruocco, Michail Spitieris","submitted_at":"2025-05-25T21:12:01Z","abstract_excerpt":"Recent advances in generative AI offer promising solutions for synthetic data generation but often rely on large datasets for effective training. To address this limitation, we propose a novel generative model that learns from limited data by incorporating physical constraints to enhance performance. Specifically, we extend the VAE architecture by incorporating physical models in the generative process, enabling it to capture underlying dynamics more effectively. While physical models provide valuable insights, they struggle to capture complex temporal dependencies present in real-world data. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.19320","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/2505.19320/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:09:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wu6/a+Psg6jVDh1L6w8VERRo+uxbH1BhY30iCVx8GRsyO6ZFdxfqSYyHnzAR05HyffO9uc5PkOcKT1OhAB3sDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T15:16:21.861894Z"},"content_sha256":"457296d54deb87981bd7b817d7979b1ba42a8578252a3f38402d3f8b151239fc","schema_version":"1.0","event_id":"sha256:457296d54deb87981bd7b817d7979b1ba42a8578252a3f38402d3f8b151239fc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PLPXWC2ZZNILBF4GMARUHV5PTP/bundle.json","state_url":"https://pith.science/pith/PLPXWC2ZZNILBF4GMARUHV5PTP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PLPXWC2ZZNILBF4GMARUHV5PTP/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":"2026-08-09T15:16:21Z","links":{"resolver":"https://pith.science/pith/PLPXWC2ZZNILBF4GMARUHV5PTP","bundle":"https://pith.science/pith/PLPXWC2ZZNILBF4GMARUHV5PTP/bundle.json","state":"https://pith.science/pith/PLPXWC2ZZNILBF4GMARUHV5PTP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PLPXWC2ZZNILBF4GMARUHV5PTP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:PLPXWC2ZZNILBF4GMARUHV5PTP","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":"55cb68e3a09d3ccf24f6b107ed52cbac7f0be75c1258ecc56b389f1753c275df","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-05-25T21:12:01Z","title_canon_sha256":"7746e4937ad3d16d075ed8ba2f52d133604cad7e49d2a8b5c5a870c92d82589a"},"schema_version":"1.0","source":{"id":"2505.19320","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.19320","created_at":"2026-07-05T11:09:28Z"},{"alias_kind":"arxiv_version","alias_value":"2505.19320v1","created_at":"2026-07-05T11:09:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.19320","created_at":"2026-07-05T11:09:28Z"},{"alias_kind":"pith_short_12","alias_value":"PLPXWC2ZZNIL","created_at":"2026-07-05T11:09:28Z"},{"alias_kind":"pith_short_16","alias_value":"PLPXWC2ZZNILBF4G","created_at":"2026-07-05T11:09:28Z"},{"alias_kind":"pith_short_8","alias_value":"PLPXWC2Z","created_at":"2026-07-05T11:09:28Z"}],"graph_snapshots":[{"event_id":"sha256:457296d54deb87981bd7b817d7979b1ba42a8578252a3f38402d3f8b151239fc","target":"graph","created_at":"2026-07-05T11:09:28Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2505.19320/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent advances in generative AI offer promising solutions for synthetic data generation but often rely on large datasets for effective training. To address this limitation, we propose a novel generative model that learns from limited data by incorporating physical constraints to enhance performance. Specifically, we extend the VAE architecture by incorporating physical models in the generative process, enabling it to capture underlying dynamics more effectively. While physical models provide valuable insights, they struggle to capture complex temporal dependencies present in real-world data. ","authors_text":"Abdulmajid Murad, Alessandro Nocente, Massimiliano Ruocco, Michail Spitieris","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-05-25T21:12:01Z","title":"PIGPVAE: Physics-Informed Gaussian Process Variational Autoencoders"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.19320","kind":"arxiv","version":1},"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":"sha256:4e7332c7e472864129eefb288d2b126682a1167e5692b7f63061b67f390eb1d8","target":"record","created_at":"2026-07-05T11:09:28Z","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":"55cb68e3a09d3ccf24f6b107ed52cbac7f0be75c1258ecc56b389f1753c275df","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-05-25T21:12:01Z","title_canon_sha256":"7746e4937ad3d16d075ed8ba2f52d133604cad7e49d2a8b5c5a870c92d82589a"},"schema_version":"1.0","source":{"id":"2505.19320","kind":"arxiv","version":1}},"canonical_sha256":"7adf7b0b59cb50b09786602343d7af9bc3186120e02bc6e3106922b3948219ea","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7adf7b0b59cb50b09786602343d7af9bc3186120e02bc6e3106922b3948219ea","first_computed_at":"2026-07-05T11:09:28.270482Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:09:28.270482Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bYf0dfc37xi1Zi3Zx1dinZZ64EXJ3AkgdpOzYFPsVvz2niy2TBe2GwNjpVjZHkRUprh3Ri5UTaDYlVUTIY/vAg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:09:28.271162Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.19320","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4e7332c7e472864129eefb288d2b126682a1167e5692b7f63061b67f390eb1d8","sha256:457296d54deb87981bd7b817d7979b1ba42a8578252a3f38402d3f8b151239fc"],"state_sha256":"e316ef6fd85227423b8cf969b18b3e7e7c1eae477800f4d70e4ab9809d46e207"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"o6oXCqWsb/ydOrF/aAPrpWJEvEI0JCXzidTCN7GF7MKCgSQ/IUKfDd9EHXvVx7RAG+GR7mHW9hscWHm2uM4wCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T15:16:21.866884Z","bundle_sha256":"484f3b60664cf87d2e16dad8659701147afde438a9b724237831b3d8c06e7a32"}}