DIANNA
DI
Deep Insight And Neural Network Analysis, DIANNA is the only Explainable AI, XAI library for scientists supporting Open Neural Network Exchange, ONNX - the de facto standard models format.
Updated 1 month ago
21 12
image credit: Photo by USGS on Unsplash (https://unsplash.com/photos/eFbxYl9M_lc)
Society is vulnerable to weather extremes like heatwaves and droughts, and current operational forecast systems are typically only accurate up to 10 days ahead. New, massive climate datasets from satellites and climate models, combined with novel machine learning techniques provide great promise to push that forecast horizon further, potentially to several weeks or months ahead. To achieve this, we want to develop code that enables reproducible analyses according to best practices, which also provides insights into where the predictability is coming from.
AI4S2S builds an open-source python package that can efficiently run across different Big Climate Data platforms and that will include the latest advances in machine learning. We will actively involve experts worldwide to generate a sustainable, community-driven coding effort, via dedicated workshops and online outreach. AI4S2S has the potential to make a huge impact on research (enabling scientific breakthroughs), education (lowering technical barriers) and society (forecast-based risk reduction).
This page covers two projects, OEC 2021 project titled AI4S2S and SS project titled "Artificial Intelligence for S2S scientists".