Semantic Scholar
metadata only
Enhanced data efficiency using deep neural networks and Gaussian processes for aerodynamic design optimization
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
- Bayesian Optimization of a Lightweight and Accurate Neural Network for Aerodynamic Performance Prediction#997957
- Physics-informed data-driven unsteady Reynolds-averaged Navier-Stokes turbulence modeling for particle-laden jet flows#1005507
- Hardware-conscious Software Training for Deep Neural Network Inference Accelerator Chips to Recover Accuracy Degradation due to Hardware Variabilities#927559
- Unified nonlinear modeling approach for machine learning and artificial … — Nxgen Partners Ip, Llc (US12488266B2)#607072
- Artificial intelligence techniques: Machine learning and deep learning algorithms#10792
Record · ID 135247
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