arXiv (All)
open access
D-IMPL: A Diffusion-based Solver for Parameterized BBOs
Diffusion models have demonstrated strong power in generative modeling tasks across multiple domains, exhibiting a remarkable capability of learning complex distributions from samples. In this paper, we leverage such capability to design an efficient universal diffusion-based solver for parameterize
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
- Single Image to Textured 3D Object Generation in Frequency Domain: From Theory to Pipeline#975149
- Where to Compute and How to Interact: Operator-Readable Adaptation with Gauge-Aware Transport#1109605
- Massively parallel transdimensional sampling with data assimilation: an application to LISA galactic binaries#1011402
- Climate Physics Dynamic Matching#780764
- Climate Physics Dynamic Matching#779389
Record · ID 1037435
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