works on

From the 1 of 5 linked papers with an AI index.

most citedAIMIP Phase 1: systematic evaluations of AI weather and climate models

1 citations · 1 across the 2 of their papers we have counts for

collaborators

5 papers

cs.LG2026

Kastor: An efficient fine-tuning strategy for generative emulation of PDE simulations

Guillaume Couairon, Alexis Jacq, Yu-Han Wu +4

Machine learning offers a promising avenue to accelerate physical simulations by replacing computationally expensive traditional Partial Differential Equation (PDE) solvers with fa…

physics.ao-ph20261 cited

AIMIP Phase 1: systematic evaluations of AI weather and climate models

Brian Henn, Christopher S. Bretherton, Nikolay Koldunov +18

The paper introduces AIMIP Phase 1, an intercomparison framework for AI‑based weather and climate models that evaluates their ability to simulate historical atmospheric conditions…

physics.ao-ph2026

Evaluating Skill and Stability of ArchesWeather and ArchesWeatherGen under Multi-Decadal Climate Simulations

Renu Singh, Robert Brunstein, Antonia Jost +5

We evaluate the climate simulation capabilities of ArchesWeather and ArchesWeatherGen, two machine learning models originally trained for weather forecasting and evaluated up to a…

cs.CV2025

Scaling 4D Representations

João Carreira, Dilara Gokay, Michael King +32

Scaling has not yet been convincingly demonstrated for pure self-supervised learning from video. However, prior work has focused evaluations on semantic-related tasks $\unicode{x20…

cs.CV2025

SciVid: Cross-Domain Evaluation of Video Models in Scientific Applications

Yana Hasson, Pauline Luc, Liliane Momeni +10

In recent years, there has been a proliferation of spatiotemporal foundation models in different scientific disciplines. While promising, these models are often domain-specific and…