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Sparse Sensor Placement Optimization for Classification
Choosing a limited set of sensor locations to characterize or classify a high-dimensional system is an important challenge in engineering design. Traditionally, optimizing the sensor locations involves a brute-force, combinatorial search, which is NP-hard and is computationally intractable for even moderately large problems. Using recent advances in sparsity-promoting techniques, we present a novel algorithm to… Read more
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Discovering governing equations from data: Sparse identification of nonlinear dynamical systems
The ability to discover physical laws and governing equations from data is one of humankind’s greatest intellectual achievements. A quantitative understanding of dynamic constraints and balances in nature has facilitated rapid development of knowledge and enabled advanced technological achievements, including aircraft, combustion engines, satellites, and electrical power. In this work, we combine sparsity-promoting techniques and… Read more
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SitRep 16: COVID-19 transmission across Washington State
Data through Sept. 3 indicate that while cases overall appear to be decreasing, trends are not unfiorm and are plateauing or even increasing in several counties. Read more
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The scaling of contact rates with population density for the infectious disease models
Contact rates and patterns among individuals in a geographic area drive transmission of directly-transmitted pathogens, making it essential to understand and estimate contacts for simulation of disease dynamics. Under the uniform mixing assumption, one of two mechanisms is typically used to describe the relation between contact rate and population density: density-dependent or frequency-dependent. Based on… Read more