HAL (France)
open access
Learning structural damage fields from time-domain wave propagation data with Bayesian neural networks
This study investigates the application of Bayesian Neural Networks for regression tasks aimed at recovering structural damage parameters from time-domain wave propagation data. The raw wave propagation data are simulated in a one-dimensional elastic bar using the Spectral Finite Element Method, con
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
- An innovative approach to structural damage identification via dictionary learning technologies#480348
- Extreme Learning Machine-based Barrier Function Synthesis#1144188
- Machine Learning Methods for Inverse Problems in Structural Health Monitoring#642220
- Parallelized adaptive Bayesian updating with structural reliability methods for inference of large engineering models#1161283
- Physics-Constrained, Structure-Preserving Machine Learning Models for Structural Health Applications#983566
Record · ID 1390767
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