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Abstract: Most problems in Earth sciences aim to do inferences about the system, where accurate predictions are just a tiny part of the whole problem. Inferences mean understanding variables relations, deriving models that are physically plausible, that are simple parsimonious, and mathematically tractable. While machine learning models excel as approximators, they often disregard fundamental physics […]
Multimodal foundation models are a revolutionary class of AI models that provides impressive abilities to generate content (text, images, sound, videos, protein structures, and more), and do so by interactive prompts in a seemingly creative manner. These foundation models are often autoregressive, self-supervised, transformer-based models that are pre-trained on large volumes of data, typically collected […]
Abstract: For a better understanding of the Earth system we need a stronger integration of observations and (mechanistic) models. Classical model-data integration approaches start with a model structure and try to estimate states or parameters via data assimilation and inverse modelling, respectively. Sometimes, several model structures are employed and evaluated, e.g. in Bayesian model averaging, […]
Theme This workshop aims to initiate tangible actions supported by ELIAS and ELISE to empower the next generation of talent, establish foundational models, and foster startups. Key measures the projects seek to align on include an annual meeting on open source foundation models and sciencepreneurship, a European graduate project programme, a pan-EU incubation programme, and a call for sciencepreneur […]
