This thesis tackles the subject of spatio-temporal forecasting with deep
learning. The motivating application at Electricity de France (EDF) is
short-term solar energy forecasting with fisheye images. We explore two main
research directions for improving deep forecasting methods by injecting
external physical knowledge. The first direction concerns the role of the
training loss function. We show that differentiable shape and temporal criteria
can be leveraged to improve the performances of existing models. We address
both the deterministic context with the proposed DILATE loss function and the
probabilistic context with the STRIPE model. Our second direction is to augment
incomplete physical models with deep data-driven networks for accurate
forecasting. For video prediction, we introduce the PhyDNet model that
disentangles physical dynamics from residual information necessary for
prediction, such as texture or details. We further propose a learning framework
(APHYNITY) that ensures a principled and unique linear decomposition between
physical and data-driven components under mild assumptions, leading to better
forecasting performances and parameter identification.

本文研究了基于深度学习的时空预测问题，并探索使用物理知识来改进深度预测方法的两个主要研究方向：一是使用训练损失函数来改进模型的性能，包括确定性情况和概率情况；另一个是通过深度数据驱动网络来增强不完备的物理模型，以实现准确的预测。