{"paper":{"title":"Diagnosing Failure Modes of Neural Operators Across Diverse PDE Families","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Strong in-distribution accuracy does not reliably predict robustness in neural PDE solvers across architectures and equation families.","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Lennon Shikhman","submitted_at":"2026-01-16T16:47:44Z","abstract_excerpt":"Neural PDE solvers are increasingly used as learned surrogates for families of partial differential equations, where the key machine learning challenge is not only interpolation on a fixed benchmark distribution but generalization under structured shifts in coefficients, boundary conditions, discretization, and rollout horizon. Yet evaluation is still often dominated by in-distribution test error, making robustness difficult to assess. We introduce a standardized stress-testing framework for neural PDE solvers under deployment-relevant shift. We instantiate it on three representative architect"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"strong in-distribution accuracy does not reliably predict robustness, and that failure patterns depend jointly on architecture and PDE family.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The chosen shifts in coefficients, boundary conditions, discretization, and rollout horizon, together with the five selected PDE families, are representative of deployment-relevant distribution shifts.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A new stress-testing framework reveals that in-distribution accuracy does not reliably predict robustness of neural operators across diverse PDE families and architectures.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Strong in-distribution accuracy does not reliably predict robustness in neural PDE solvers across architectures and equation families.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"220cc0c2c815c2d96d592fb4fae4f9f0db320b2ef6f7027a38ab099c60c6fda4"},"source":{"id":"2601.11428","kind":"arxiv","version":7},"verdict":{"id":"0b08e979-5530-48f0-9d97-d5ef6a929211","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-16T13:24:56.456710Z","strongest_claim":"strong in-distribution accuracy does not reliably predict robustness, and that failure patterns depend jointly on architecture and PDE family.","one_line_summary":"A new stress-testing framework reveals that in-distribution accuracy does not reliably predict robustness of neural operators across diverse PDE families and architectures.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The chosen shifts in coefficients, boundary conditions, discretization, and rollout horizon, together with the five selected PDE families, are representative of deployment-relevant distribution shifts.","pith_extraction_headline":"Strong in-distribution accuracy does not reliably predict robustness in neural PDE solvers across architectures and equation families."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2601.11428/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"}