arXiv (All)
open access
Mind the Gap: Navigating Inference with Optimal Transport Maps
Machine learning (ML) techniques have recently enabled enormous gains in sensitivity to new phenomena across the sciences. In particle physics, much of this progress has relied on excellent simulations of a wide range of physical processes. However, due to the sophistication of modern machine learni
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
- Certifying Learned Variables#713973
- PTNO: Training Neural Operators with Noisy Monte Carlo Estimates for Particle Transport Problems#1154359
- PTNO: Training Neural Operators with Noisy Monte Carlo Estimates for Particle Transport Problems#1136839
- Simulation codes and data for "Learning Hydro-Phoretic Interactions Enables Large-Scale Active Matter Simulations"#479710
- Physical knowledge on historical data matters more than enforcing physical constraints on the forecast#1014918
Record · ID 974208
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