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Predictive Scale-Bridging Simulations through Active Learning
Throughout computational science, there is a growing need to utilize the continual improvements in raw computational horsepower to achieve greater physical fidelity through scale-bridging over brute-force increases in the number of mesh elements.
Machine Learning-Powered Mitigation Policy Optimization in Epidemiological Models
A crucial aspect of managing a public health crisis is to effectively balance prevention and mitigation strategies, while taking their socio-economic impact into account.
Accurate Calibration of Agent-based Epidemiological Models with Neural Network Surrogates
Here we present a new approach to calibrate an agent-based model -- EpiCast -- using a large set of simulation ensembles for different major metropolitan areas of the United States.
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