Advancing the Computational Frontiers of Solution-Adaptive, Scale-Aware Climate Models
Researchers: Christiane Jablonowski, Climate and Space Sciences and Engineering; Hans Johansen, Lawrence Berkeley National Lab
Description: Researchers will further develop a 3-D mesh adaptation model for climate modeling, allowing computational resources to be focused on phenomena of interest such as tropical cyclones or other extreme weather events. The project will also introduce data-driven machine learning paradigms into modeling of clouds and precipitation.
Structure of a block-structured adaptive grid that overlays a so-called ‘cubed-sphere’ base grid. The atmospheric vortices (in color) resemble idealized tropical cyclones that are captured by the refined grid patches. The adaptations are guided by the location and strength of the rotational motion.
Embedded Machine Learning Systems To Sense and Understand Pollinator Behavior
Researchers: Robert Dick, Electrical Engineering and Computer Science; Fernanda Valdovinos Ecology and Evolutionary Biology, Center for Complex Systems; Paul Glaum, Ecology and Evolutionary Biology
Description: To understand the mechanisms driving the population dynamics of pollinators, the research team will develop technologies for deeply embedded hardware/software learning systems capable of remote, long term, autonomous operation; and will analyze the resulting new data to better understand pollinator activity.
Embedded sensing and machine learning to distinguish pollinators (Bombus impatiens pictured) from other sound sources in natural environments.
Deep Learning for Phylogenetic Inference
Researcher: Jianzhi Zhang, Ecology and Evolutionary Biology; Yuanfang Guan, Computational Medicine and Bioinformatics
Description: The research team will use deep neural networks to infer molecular phylogenies and extract phylogenetically useful patterns from amino acid or nucleotide sequences, which will help understand evolutionary mechanisms and build evolutionary models for a variety of analyses.
Molecular sequence data are input into layers of neural network structures to produce a score for each possible tree topology describing how well the tree fits the data.