{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:RGRGVMJQAXZAYIS6JONXXRX5Y7","short_pith_number":"pith:RGRGVMJQ","schema_version":"1.0","canonical_sha256":"89a26ab13005f20c225e4b9b7bc6fdc7f66ef7fd1a88c97fe3d61439273a532b","source":{"kind":"arxiv","id":"2109.08815","version":2},"attestation_state":"computed","paper":{"title":"Probabilistic Inference of Simulation Parameters via Parallel Differentiable Simulation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Christopher E. Denniston, David Millard, Eric Heiden, Fabio Ramos, Gaurav S. Sukhatme","submitted_at":"2021-09-18T03:05:44Z","abstract_excerpt":"To accurately reproduce measurements from the real world, simulators need to have an adequate model of the physical system and require the parameters of the model be identified.\n  We address the latter problem of estimating parameters through a Bayesian inference approach that approximates a posterior distribution over simulation parameters given real sensor measurements. By extending the commonly used Gaussian likelihood model for trajectories via the multiple-shooting formulation, our chosen particle-based inference algorithm Stein Variational Gradient Descent is able to identify highly nonl"},"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":"2109.08815","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2021-09-18T03:05:44Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"e670c4502cb6673a18cf292a6da043c33aea07a4f06a7550cd60f7d1d4605a6b","abstract_canon_sha256":"d79ddbe19baf68f1aad132f8f2e3084aace5f03356b1be64604b0749ebb0c20d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:00:18.097820Z","signature_b64":"xixr/Le8XxxVINrVUmp8z6e0I+hTZoFccdfhDIHkj5feebgo2v2MKjRLZMIdVXI4DmIHWBJ1elPSlpMninmHAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"89a26ab13005f20c225e4b9b7bc6fdc7f66ef7fd1a88c97fe3d61439273a532b","last_reissued_at":"2026-07-05T04:00:18.097307Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:00:18.097307Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Probabilistic Inference of Simulation Parameters via Parallel Differentiable Simulation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Christopher E. Denniston, David Millard, Eric Heiden, Fabio Ramos, Gaurav S. Sukhatme","submitted_at":"2021-09-18T03:05:44Z","abstract_excerpt":"To accurately reproduce measurements from the real world, simulators need to have an adequate model of the physical system and require the parameters of the model be identified.\n  We address the latter problem of estimating parameters through a Bayesian inference approach that approximates a posterior distribution over simulation parameters given real sensor measurements. By extending the commonly used Gaussian likelihood model for trajectories via the multiple-shooting formulation, our chosen particle-based inference algorithm Stein Variational Gradient Descent is able to identify highly nonl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.08815","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/2109.08815/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":"2109.08815","created_at":"2026-07-05T04:00:18.097385+00:00"},{"alias_kind":"arxiv_version","alias_value":"2109.08815v2","created_at":"2026-07-05T04:00:18.097385+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.08815","created_at":"2026-07-05T04:00:18.097385+00:00"},{"alias_kind":"pith_short_12","alias_value":"RGRGVMJQAXZA","created_at":"2026-07-05T04:00:18.097385+00:00"},{"alias_kind":"pith_short_16","alias_value":"RGRGVMJQAXZAYIS6","created_at":"2026-07-05T04:00:18.097385+00:00"},{"alias_kind":"pith_short_8","alias_value":"RGRGVMJQ","created_at":"2026-07-05T04:00:18.097385+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.00589","citing_title":"Constrained Stein Variational Gradient Descent for Robot Perception, Planning, and Identification","ref_index":31,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RGRGVMJQAXZAYIS6JONXXRX5Y7","json":"https://pith.science/pith/RGRGVMJQAXZAYIS6JONXXRX5Y7.json","graph_json":"https://pith.science/api/pith-number/RGRGVMJQAXZAYIS6JONXXRX5Y7/graph.json","events_json":"https://pith.science/api/pith-number/RGRGVMJQAXZAYIS6JONXXRX5Y7/events.json","paper":"https://pith.science/paper/RGRGVMJQ"},"agent_actions":{"view_html":"https://pith.science/pith/RGRGVMJQAXZAYIS6JONXXRX5Y7","download_json":"https://pith.science/pith/RGRGVMJQAXZAYIS6JONXXRX5Y7.json","view_paper":"https://pith.science/paper/RGRGVMJQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2109.08815&json=true","fetch_graph":"https://pith.science/api/pith-number/RGRGVMJQAXZAYIS6JONXXRX5Y7/graph.json","fetch_events":"https://pith.science/api/pith-number/RGRGVMJQAXZAYIS6JONXXRX5Y7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RGRGVMJQAXZAYIS6JONXXRX5Y7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RGRGVMJQAXZAYIS6JONXXRX5Y7/action/storage_attestation","attest_author":"https://pith.science/pith/RGRGVMJQAXZAYIS6JONXXRX5Y7/action/author_attestation","sign_citation":"https://pith.science/pith/RGRGVMJQAXZAYIS6JONXXRX5Y7/action/citation_signature","submit_replication":"https://pith.science/pith/RGRGVMJQAXZAYIS6JONXXRX5Y7/action/replication_record"}},"created_at":"2026-07-05T04:00:18.097385+00:00","updated_at":"2026-07-05T04:00:18.097385+00:00"}