Cluster detection is important and widely used in a variety of applications, including public health, public safety, transportation, and so on. Given a collection of data points, we aim to detect density-connected spatial clusters with varying geometric shapes and densities, under the constraint that the clusters are statistically significant. The problem is challenging, because many societal applications and domain science studies have low tolerance for spurious results, and clusters may have arbitrary shapes and varying densities. As a classical topic in data mining and learning, a myriad of techniques have been developed to detect clusters with both varying shapes andmore »
Emulating numeric hydroclimate models with physics-informed conditional generative adversarial networks.
Process-based numerical simulation, includ- ing for climate modeling applications, is compute- and resource-intensive, requiring extensive customization and hand-engineering for encoding governing equations and other domain knowledge. On the other hand, modern deep learning employs a much simplified and efficient computational workflow, and has been showing impres- sive results across myriad applications in computational sciences. In this work, we investigate the potential of deep generative learning models, specifically conditional Gen- erative Adversarial Networks (cGANs), to simulate the output of a physics-based model of the spatial distribution of the water content of mountain snowpack, or snow water equivalent (SWE). We show preliminary results indicating that the cGANs model is able to learn map- pings between meteorological forcing (e.g., minimum and maximum temperature, wind speed, net radiation, and precipitation) and SWE output. Moreover, informing the model with simple domain-inspired physical constraints results in higher model accuracy, and lower training time. Thus Physics-Informed cGANs provide a means for fast and accurate SWE modeling that can have significant impact in a variety of applications (e.g., hydropower forecasting, agriculture, and water supply management).
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