Events

Over the past decade, the integration of Artificial Intelligence (AI) for scientific exploration has grown as a transformative force, propelling research into new realms of discovery. The AI4DifferentialEquations in Science workshop at ICLR 2024 invites participants on a dynamic journey at the interface of machine learning and computational sciences known as Scientific Machine Learning (SciML). [more]

[more]

[more]

Artificial intelligence has been on everyone’s lips for more than a year, as it has made creative tasks possible, such as automatic writing of texts (e.g., ChatGPT) or generating images (e.g., Dall-E). The panel will discuss whether the development that has led to the so-called generative models can also be used to address the impending challenges of climate change for society. Specifically, it examines the possibilities of today’s AI for early warning and decision-making systems in the context of climate, environmental, and health risks. [more]

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, but still parametric model structures are assumed.Recently, Reichstein et al. (2019) proposed a fusion of machine learning and mechanistic modelling approaches into so-called hybrid modelling. Ideally, this combines scientific consistency with the versatility of data driven approaches and is expected to allow for better predictions and better understanding of the system, e.g. by inferring unobserved variables. This talk will elaborateon developments of this concept and illustrate its promise but also challenges with examples on biosphere-atmosphere exchange, and carbon and water cycles from the ecosystem to the global scale. [more]