arxiv
PublishedJune 18, 2026 at 4:00 AM
▲bullish
Optimal scenario design for climate emulation
Publisher summary· verbatim
arXiv:2606.19302v1 Announce Type: cross Abstract: As deep learning for physical systems continues to grow in popularity, efforts to improve generalizability have primarily focused on designing architectures that embed physical constraints. However, for machine-learning surrogate climate models (emul
Stay posted· Newsletter
A 5-min weekly brief — top movers, price watch, story of the week.
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivBringing Value Models Back: Generative Critics for Value Modeling in LLM Reinforcement Learning4harxivSubagents vs Agent Skills: Executing Reusable Knowledge for Long-Horizon Agentic Tasks4harxivDistribution-Consistent Inference for Dynamic Sparse Mixture-of-Experts4harxivIn RAG We Trust? Measuring Robustness of Retrieval-Augmented Generation Under Document Poisoning4hThe Bubble Brief
WEEKLYRead climate-modeling insights every Tuesday — top movers, new releases, story of the week.
Originally published on arxiv ↗