ODU Digital Commons
metadata only
A Survey of AI Methods for Geometry Preparation and Mesh Generation in Engineering Simulation
Artificial intelligence is beginning to reduce the manual effort in the CAD-to-mesh pipeline. Written for meshing and geometry practitioners with limited AI background, this survey organizes recent work by workflow step. We cover part classification and segmentation, mesh quality prediction, and def
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
- VoroUDF: Meshing Unsigned Distance Fields with Voronoi Optimization#1049749
- Native3D: End-to-End 3D Scene Generation via Unified Mesh-Texture Modeling and Semantic Alignment#266232
- Advanced 3D Microstructure Generation of Solid Oxide Cell Electrodes Using Conditional Generative Adversarial Network and Validation Using Nonintuitive Topological Characteristics#625642
- Advanced 3D Microstructure Generation of Solid Oxide Cell Electrodes Using Conditional Generative Adversarial Network and Validation Using Nonintuitive Topological Characteristics#625619
- Generating Heterogeneous 3D Geological Microstructures from 2D Images via a Stable Diffusion-Adversarial Model#1034122
Record · ID 1416006
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