We present graph-based neural surrogates that enable fast and uncertainty-aware emulation of global hybrid-Vlasov simulations via a probabilistic latent-variable model. Data + code are openly available, and compatible with advances happening in data-driven weather prediction!
Are you interested in ML for space/plasma physics? I'll be presenting “Graph-based Neural Space Weather Forecasting” this Saturday at the ML for physics workshop in San Diego @neuripsconf.bsky.social 🚀
📄 Paper: arxiv.org/abs/2509.19605
💻 Code: github.com/fmihpc/space...
#AI4Science #ML4PS
I am recruiting PhD students for 2026!😃
You want to reveal the geometric signatures of natural and artificial intelligence, and understand computations in brains and AI? 🌐🧠🤖
Apply to the UCSB Geometric Intelligence Lab ✨
This is the view you'd have from... your desk🌴
ECMWF with two new papers right before christmas.
AIFS-CRPS: arxiv.org/abs/2412.158...
GraphDOP (the first truly end2end global weather model): arxiv.org/abs/2412.15687
Here they are added to the SotA tracker: docs.google.com/spreadsheets...
Thanks! 😄
Hi, possible to be added here? Researching ML & ocean
🧵 Today with @polymathicai.bsky.social and others we're releasing two massive datasets that span dozens of fields - from bacterial growth to supernova!
We want this to enable multi-disciplinary foundation model research.
Scientific poster with dark-blue background. Title: Flow Annealed Importance Sampling Bootstrap meets differentiable particle physics. The poster contains multiple sections: Generation of data in particle physics, normalizing flows, training approaches, and results for a 2D distribution (lambda_c^+ decay) and a 8D distribution (top production). Additionally, results for the efficiency over the number of target evaluations are shown.
1/ 🚀 New Paper Alert: Spotlight at NeurIPS ML and the Physical Sciences Workshop!
We explore the intersection of high-energy physics and machine learning. What's the challenge we’re targeting, and why does it matter? Let's dive in! 🧵👇
🚀 #AI #MachineLearning #Physics #ML4PS #NeurIPS #AcademicSky
The results show that SeaCast provides:
🔎 Accurate SST forecast with respect to satellite observations.
📊 On par skill compared to the operational forecasting system at several depth levels.
🚀 Faster predictions than a physics-based model, with a complete forecast taking 11 seconds on a single GPU.
Our model has been trained to predict physical quantities in the Mediterranean Sea up to 15 days ahead at a high 1/24° spatial resolution, using open data from Copernicus Marine Service produced by the CMCC Foundation. Both numerical and data-driven forecasts by ECMWF are tested as surface forcings.
🌊 Meet SeaCast, a new, openly available graph neural network for regional ocean forecasting.
Paper: arxiv.org/abs/2410.11807.
Looking forward to presenting this work at the Climate Change AI workshop taking place at @neuripsconf.bsky.social in December!
🧵 Small thread below.
