Recent advances in artificial intelligence (AI) have produced unexpected and impressive results for weather forecasting, despite the complexity of these multi-scale phenomena. AI is also playing an increasingly important role in climatology. These results raise profound questions about modelling. On the one hand, we know the physics equations that have so far been used in large-scale numerical models. On the other hand, many physical parameters are unknown, for example at interfaces, which motivates a learning approach based on past data. We can also learn the evolution equations indirectly, eliminating the need for physical modelling. The approaches developed in AI in recent years oscillate between these two strategies.
This conference will present the state of the art at the interface between mathematics, physics and statistical learning using deep neural networks. It will highlight the advantages and disadvantages of the different modelling strategies, as well as the a priori incorporated in the form of equations or algorithmic architectures, in relation with physics. An objective is to encourage a dialogue between mathematicians, AI researchers, meteorologists and climatologists.
The event is in English.
The Avenir Commun Durable initiative is supported by the Collège de France Foundation, its major sponsors Fondation Covéa and TotalEnergies, and its patrons FORVIA and Saint-Gobain.
