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"paper": {
"id": "Xg12D1Y4H1",
"title": "Scalable Bayesian Inference for Nonlinear Conservation Laws",
"arxiv_id": "2605.31127",
"version": 1,
"pdf_sha256": "20f385c9da8f1449891164372e5fa0efe6afce9687754223a43902bd80c2a1f1",
"source_sha256": "86302b8afeaf6f59578302dbeb5ad467197c1ad287989283d73aa7bd5210b497"
},
"official_code": {
"repository": "https://github.com/timweiland/GPFiniteVolume.jl",
"commit": "d711fbb68ec74755ef4029301472dfe8e23622de",
"tree": "c41a87185cb5c5a7df0edf36fc7458a503c47018",
"project_sha256": "83cee925d3a9acebffeb609e3e1eccc6386bb038981a22c871839bf5f5a9bbd2",
"manifest_sha256": "9d2bccfc8c3ba756879380a6943efe6deb320a552b7589a4ff1c50b68d9e6e21",
"functional_gps_commit": "43a58a63f9dd4f09916d6f91a307ca15eee5a7bf"
},
"native_environment": {
"julia": "1.10.10",
"execution_platform": "Apple host CPU",
"manifest_instantiated_exactly": true,
"upstream_tests": "8/8 passed"
},
"key_files": {
"official_code/experiments/source_identification/run_gpfvm.jl": "bcc11143b39a5969f782860b03bc29eb18527660d3cb577b1c8f9beb7d91a046",
"official_code/experiments/poisson_convergence.jl": "6393f03a686035cb53a7b56e30a4c0f6b252411f90e827c308153a55fae730d1",
"official_code/experiments/sparsity_accuracy_experiment.jl": "4ab2a0e27c32cfe85fc298ae9d8009a4e3192dce67158dcf7ee3bf9ebc3ba27d",
"outputs/source_identification_input.npz": "502f3464cb65d147ee7d56859aa0483afcd879898eb8dd3dca9cb504df3b26df",
"outputs/source_identification_native_result.npz": "f8d7b5ed3f294c5237081f3f3b3f227852e7e99b24f23f169fdfc19a268e1177"
},
"scope": "The package contains the complete pinned official source tree, complete arXiv-v1 PDF/source archive, source-native paper-scale outputs, and deterministic independent reanalysis. It does not include or claim an execution of the unreleased JAX PINN baseline."
}
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