arXiv (All)
open access
Neural-Surrogate Co-Design of Aerodynamic Shape and Control
Control co-design jointly optimizes a robot's physical design and its controller. Existing techniques are limited in the number of model parameters and the complexity of the physical models they can handle. In this paper, we propose a gradient-based bilevel formulation that replaces the dynamics mod
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
- BILEVEL OPTIMIZATION FOR FAST CONTROL MOMENT GYRO MANEUVERS#497040
- Funnel planning and control for manipulators using neural Signed Configuration Distance Functions#1325813
- Differentiable optimization for robotics : simulation, learning and control#377944
- Control Allocation with Adaptive Augmentation for Aerodynamic Optimization of Trailing Edge Morphing Aircraft#1156593
- A Review of Bilevel Optimization: Methods, Emerging Applications, and Recent Advancements#1117272
Record · ID 1343523
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