Spatial Field

Spatial field reconstruction aims to estimate a complete spatial field from sparse or incomplete observations, a crucial task across diverse scientific domains. Current research heavily utilizes machine learning, particularly deep neural networks (including convolutional and other architectures) and diffusion models, often incorporating techniques like Kriging and optimal transport for improved accuracy and efficiency compared to traditional physics-based methods. These advancements enable faster and more robust predictions in applications ranging from environmental monitoring and fluid dynamics to climate modeling and precision agriculture, impacting fields that rely on high-resolution spatial data. The focus is on handling noisy data, improving computational scalability for large datasets, and incorporating uncertainty quantification.