Predicting wind-driven spatial deposition through simulated color images using deep autoencoders.

ABSTRACT: For centuries, scientists have observed nature to understand the laws that govern the physical world. The traditional process of turning observations into physical understanding is slow. Imperfect models are constructed and tested to explain relationships in data. Powerful new algorithms can enable computers to learn physics by observing images and videos. Inspired by this idea, instead of training machine learning models using physical quantities, we used images, that is, pixel information. For this work, and as a proof of concept, the physics of interest are wind-driven spatial patterns. These phenomena include features in Aeolian dunes and volcanic ash deposition, wildfire smoke, and air pollution plumes. We use computer model simulations of spatial deposition patterns to approximate im

SUBMITTER: Fernandez-Godino MG 

PROVIDER: S-EPMC9876895 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature