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    Potential Scenario
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    2002-Dietz-Heesterbeek-Daniel Bernoulli-epidemiological model revisited
    The seminal paper by Daniel Bernoulli published in 1766 is put into a new perspective. After a short account of smallpox inoculation and of Bernoulli’s life, the motivation for that paper and its impact are described.
    Potential Scenario
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    2015-Goodnow-EtAl-Mathematical Models of Water Clocks
    This is a historical tour of water clocks, known as clepsydra a Greek word meaning water thief. These devices were for telling time.
    Potential Scenario
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    2009-G_Recktenwald-Tank Draining Exercise
    This paper describe deep mathematics, including differential equations concerned with modeling the draining of tank and the shape of the water coming out the bore hole at the bottom of the tank of water.
    Modeling Scenario
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    9-125-BeamModeling-ModelingScenario
    This modeling scenario examines the deflection of a cantilever beam under two different distributed loads. Students will have the opportunity to conduct experiments with their own cantilever beam or use data provided to build a model.
    Potential Scenario
    192

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    2008-Lawson-Marion-An Introduction to Mathematical Modelling
    This is a broad based set of notes with sections: Building Models, Studying Models, and Using Models. No depth on any model, no solution strategies, but good references to models, and general approaches that apply in most situations are offered.
    Potential Scenario
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    2015-Beckmann-Sanchez-Cache Calculus-Modeling Caches through Differential Equations
    We present cache calculus, a technique that models cache behavior as a system of ordinary differential equations, letting standard calculus techniques find simple and accurate solutions of cache performance for common access patterns.
    Modeling Scenario
    416

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    1-039-StochasticPopModels-ModelingScenario
    We develop strategies for creating a population model using some simple probabilistic assumptions. These assumptions lead to a system of differential equations for the probability that a system is in state (or population size) n at time t.