arXiv (All)
open access
Learning When to Transfer: Reinforcement Learning for Carbon-Footprint Modeling in New Manufacturing Domains
Manufacturers often need to characterize a new material, process, or product before enough operating data are available to train reliable predictive models for this new, target domain. Existing manufacturing operations contain potentially useful knowledge to accelerate this characterization, but ind
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 When to Transfer: Reinforcement Learning for Carbon-Footprint Modeling in New Manufacturing Domains#1319022
- Artificial Intelligence Methods for Sustainable Aerospace Systems: A Review of Predictive and Generative Models#100509
- Machine Learning for Scheduling Decision Systems: A Critical Review of Architecture, Assurance, and Deployment#973018
- Sandwich-Residuals: Parameter-Efficient Test-time Adaptation of World Models#1036130
- Differentiable modelling to unify machine learning and physical models for geosciences#1366348
Record · ID 1344227
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