HAL (France)
open access
Active learning strategies for the estimation of a feasible set defined from a vector output black-box simulator
Many industrial problems require the use of black-box numerical simulators, for which it is essential to determine the set of so-called feasible input parameters. A set of parameters is feasible if the output of the code on these parameters satisfies given constraints, for example, by remaining belo
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
- Learning-Augmented Robust Algorithmic Recourse#173226
- Balancing Expressivity and Overfitting in Quantum Gaussian Process Regression#987159
- Smart Grid Security and Privacy: From Conventional to Machine Learning Issues (Threats and Countermeasures)#133991
- GUARDIAN: Safety Filtering for Systems with Perception Models Under Adversarial Attack#1084129
- Constraint-Driven Model Optimization: An Industry Framework for Selecting Compression and Acceleration Techniques in Modern Machine Learning Systems#370346
Record · ID 1251292
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