Inductive biases in deep learning models for weather prediction
Deep learning has recently gained immense popularity in the Earth sciences as it enables
us to formulate purely data-driven models of complex Earth system processes. Deep …
us to formulate purely data-driven models of complex Earth system processes. Deep …
A hybrid numerical methodology coupling Reduced Order Modeling and Graph Neural Networks for non-parametric geometries: applications to structural dynamics …
This work introduces a new approach for accelerating the numerical analysis of time-domain
partial differential equations (PDEs) governing complex physical systems. The methodology …
partial differential equations (PDEs) governing complex physical systems. The methodology …
Distributional Regression U-Nets for the Postprocessing of Precipitation Ensemble Forecasts
Accurate precipitation forecasts have a high socio-economic value due to their role in
decision-making in various fields such as transport networks and farming. We propose a …
decision-making in various fields such as transport networks and farming. We propose a …
A Sea Surface Model for Coupled Data-Driven S2S Forecasting
N Cresswell-Clay - 2023 - search.proquest.com
Data-driven modelling of the atmosphere has rapidly become a vibrant area of research.
Recent studies have shown these models have the ability to outperform existing state-of-the …
Recent studies have shown these models have the ability to outperform existing state-of-the …
[PDF][PDF] SUB-SEASONAL TO SEASONAL FORECASTS THROUGH SELF-SUPERVISED LEARNING
J Thuemmel, F Strnad, J Schlör, MV Butz… - 2024 - s3.us-east-1.amazonaws.com
Sub-seasonal to seasonal (S2S) weather forecasts are an important decision-making tool
that informs economical and logistical planning in agriculture, energy management, and …
that informs economical and logistical planning in agriculture, energy management, and …