arXiv (All)
open access
Differentiable astrophysics at scale: solving and differentiating ODE ensembles on the GPU
astrophysics-of-galaxiescomputational-physicscosmology-and-nongalactic-astrophysicsgeneral-relativity-and-quantum-cosmology
Astronomers increasingly fit their models with gradient-based methods, such as Hamiltonian Monte Carlo, which need the derivatives of the model with respect to its parameters. In many analyses a prediction requires solving a small system of ordinary differential equations (ODEs) for thousands to mil
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
- GRADSOLVE: fast exact gradients for ODE ensembles on GPUs#924992
- GRADSOLVE: fast exact gradients for ODE ensembles on GPUs#920758
- Using Machine Learning to Model Stellar Collisions in our Galactic Center#1051811
- GRADSOLVE: fast exact gradients for ODE ensembles on GPUs#632810
- GAME: Genetic Algorithms with Marginalised Ensembles for model-independent reconstruction of cosmological quantities#927236
Record · ID 1075928
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