A neural operator framework for data-driven discovery of stability and receptivity in physical systems explores A data-driven neural operator framework that automatically discovers stability and receptivity properties in complex physical systems without requiring governing equations.. Commercial viability score: 7/10 in Physics AI.
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This route is the stable paper-level surface for citations, viability, references, and downstream handoffs. Use it as the proof layer behind Signal Canvas, workspace creation, and launch-pack generation.
Page Freshness
Canonical route: /paper/a-neural-operator-framework-for-data-driven-discovery-of-stability-and-receptivity-in-physical-systems
Page-specific freshness sourced from this paper's evidence receipt and score bundle.
Agent Handoff
Canonical ID a-neural-operator-framework-for-data-driven-discovery-of-stability-and-receptivity-in-physical-systems | Route /paper/a-neural-operator-framework-for-data-driven-discovery-of-stability-and-receptivity-in-physical-systems
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/a-neural-operator-framework-for-data-driven-discovery-of-stability-and-receptivity-in-physical-systemsMCP example
{
"tool": "get_paper",
"arguments": {
"arxiv_id": "2604.19465"
}
}source_context
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"paper_ref": "a-neural-operator-framework-for-data-driven-discovery-of-stability-and-receptivity-in-physical-systems",
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}Constellation, claims, and market context stay visible on the paper proof page even when commercialization rails are held back for incomplete proof receipts.
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Dimensions overall score 7.0
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