{"paper":{"title":"NuGNN: a Graph Neural Network for Nuclear Reaction Network Equations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"nucl-th","authors_text":"C. H. Kim, K. Y. Chae, M. R. Mumpower, M. S. Smith, S. Ko","submitted_at":"2026-06-03T06:14:48Z","abstract_excerpt":"Nuclear reaction networks are a major computational bottleneck in astrophysical simulations when large isotope sets are required, because of the stiffness of the network equations and the repeated calls to Jacobian-based solvers required by implicit methods. In this work, we develop a deep learning surrogate solver for a large 690-isotope nuclear reaction network under general Type I X-ray burst conditions using a graph neural network, NuGNN. Unlike conventional fully connected or convolutional neural networks, NuGNN directly reflects the structure of the reaction network through heterogeneous"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.04491","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/2606.04491/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"}