arXiv (All)
open access
A Machine Learning-Driven Solution for Denoising Inertial Confinement Fusion Images
Neutron imaging is essential for diagnosing and optimizing inertial confinement fusion implosions at the National Ignition Facility. Due to the required 10-micrometer resolution, however, neutron image require image reconstruction using iterative algorithms. For low-yield sources, the images may be
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
- Surrogate Modeling of 3D Rayleigh-Benard Convection with Equivariant Autoencoders#1069655
- Evolution of the Magnetic Field in Accreting Neutron Stars#639357
- Wavelet-enhanced autoencoder based image denoising with CNN fusion.#239803
- Spinning down neutron-star merger remnants with the Tayler-Spruit dynamo: Global simulations reveal the formation of massive disks and neutron-rich ejecta#1130212
- Sparse Kernel feature extraction#223454
Record · ID 821415
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