arXiv (All)
open access
A-UTE: Advection Informed, Uncertainty Aware Temperature Emulator
Physics-based Earth system models (ESMs) are essential for attributing climate change and generating scenario projections, yet their reliance on high-resolution numerical integration makes multi-decadal experiments expensive. In parallel, deep learning has delivered strong gains in short-range weath
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
- EC-EarthFlow: Probabilistic emulation of daily transient global climate model simulations with flow matching#1234082
- NeuralBES: A Differentiable, Control-Aware Emulator for Scalable Building Energy Modeling#1234884
- NeuralBES: A Differentiable, Control-Aware Emulator for Scalable Building Energy Modeling#1227322
- Reduced-Order Data Assimilation for Thermospheric Density Using Physics-informed SINDyc Models#999226
- Equilibrium Training of Energy-Based Models with Parallel Trajectory Tempering#984205
Record · ID 1458157
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