HAL (France)
open access
A physics-informed AI methodology for Prognostics and Health Management of complex systems under low data constraints
Abstract Prognostics and Health Management has become a cornerstone of industrial efficiency, yet its deployment for complex systems in low-data environments remains limited. Data-driven approaches often fail due to data scarcity, while purely physics-based models are hindered by system complexity.
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Explainable Failure Prediction and Prevention in Maritime#1218098
- Roadmap: Integrating Artificial Intelligence in Structural Health Monitoring Systems#372545
- AI in Clinical Decision Support Systems: Promising Applications and Strategies for Managing Data Challenges.#154369
- Azure AI-Driven Automation for Supply Chain and Logistics Management In#100826
- AI-Powered Predictive Modeling and Computational Approaches to Nutritional Epidemiology in Low-Resource Settings: A Proof-of-Concept Framework#311469
Record · ID 1312793
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive