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    Technique Narrative
    859

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    115

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    3-090-ChebyshevPolynomialSolution-TechniqueNarrative
    The Chebyshev equation is presented as a vehicle to view series solutions techniques for linear, second order homogeneous differential equations with non-constant coefficients.
    series soluotionpolynomial solutionsChebyshev polynomialsChebyshev differential equations
    Potential Scenario
    151

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    50

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    2003-Rasmussen-Keynes-Lines of Eigenvectors and Solutions to Systems of Linear Differential Equations
    The purpose of this paper is to describe an instructional sequence where students invent a method for locating lines of eigenvectors and corresponding solutions to systems of two first order linear ordinary differential equations with constant...
    instructional designeigenvalueseigenvalue analysis
    Potential Scenario
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    2003-Bohren-Dimensional analysis-falling bodies--fine art of not solving differential equations
    Dimensional analysis is a simple, physically transparent and intuitive method for obtaining approximate solutions to physics problems, especially in mechanics.
    falling bodydimensional analysisdrag
    Potential Scenario
    120

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    34

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    2005-P_Howard-Modeling with ODE
    In these notes we consider three critical aspects in the theory of ordinary differential equations: developing models of physical phenomena, mathematically well-posed, solving ODE numerically .
    Fluid Mechanicscompartmentpopulationsvariational methodsHamiltonian mechanics
    Article or Presentation
    144

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    40

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    2015-Brian_Winkel-Informed_Conjecturing_of_Solutions_for_Differential_Equations_In_a_Modeling_Contex
    We examine (1) first order exponential growth or decay and (2) second order, linear, constant coefficient differential equations, and show the advantage of learning differential equations in a modeling context for informed conjectures of their...
    exponential growthsolutionslinear systemsdecayconjecture
    Technique Narrative
    339

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    180

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    1-003-IntroNumericalMethods-TechniqueNarrative
    We develop elementary approaches to numerically solving first order differential equations with Euler's Method, Improved Euler's Method and develop these geometrically to compute numeric solutions and compare them to analytic solutions.
    analytic solutionEuler's methodimproved Euler's method
    Technique Narrative
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    199

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    5-030-LinNonHomoSystemSol-TechniqueNarrative
    We offer strategies for solving linear systems of nonhomogeneous differential equations using a conjectured solution strategy for a system of constant coefficient, linear, n
    strategiesdiscovery learningeigenvectorseigenvalue
    Technique Narrative
    545

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    219

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    1-001-SepartionOfVariables-TechniqueNarrative
    We discuss strategies to solve first order, ordinary differential equations with mathematical models when the variables may be separated.
    solutionsstrategymethodapplications
    Potential Scenario
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    77

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    2003-Fay-Graham-Coupled spring equations
    Coupled spring equations for modelling the motion of two springs with weights attached, hung in series from the ceiling are described.
    massoscillationspring\coupled springsperiodicfporcing
    Potential Scenario
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    69

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    2002-Fay-The Pendulum Equation
    We investigate the pendulum equation q’’(t) + l2 sin(q) = 0 and two approximations for it.
    pendulumFourier seriesTaylor seriesnumerical solution
    Modeling Scenario
    363

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    228

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    1-102C-CancerGrowth-ModelingScenario
    This module guides students in the use of differential equation models to predict cancer growth and study treatment outcomes. Several classical models for cancer growth are presented including exponential, power law, Bertalanffy, logistic, and...
    optimizationcancerlogisticexponentialpredictiontumorgrowthdata fittingpower lawBertanffyGompertz
    Modeling Scenario
    250

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    133

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    3-091-SpringModeling-ModelingScenario
    In this lab students will collect data on their spring mass systems and compare their empirical models to their theoretical ones—giving them an opportunity to actually test a model against data.
    datalaboratoryLoggerProVerniermass-spring systemmodel validationempirical modeltheoretical model
    Modeling Scenario
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    151

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    5-025-SaltCompartments-ModelingScenario
    Model a phenomena in which salt mixtures from two tanks are mixed using several strategies.
    compartmentsalttanks
    Technique Narrative
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    202

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    1-009-Bifurcation-TechniqueNarrative
    We lead students to investigate first-order differential equations that contain unknown parameters. Students discover what happens to the qualitative behavior of solutions to these equations as these parameters vary.
    bifurcationqualitative behaviorbifurcation diagram
    Potential Scenario
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    51

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    2006-Michael_Coleman-Population Modeling with Ordinary Differential Equations
    We will investigate some cases of differential equations population models beyond the separable case and then expand to some basic systems of ordinary differential equations.
    population dynamics
    Potential Scenario
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    81

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    2010-Roberto_Munoz-Alicea-Mathematical Model for Enzyme Kinetics-Multiple Timescales Analysis
    We study a basic model for enzyme substrate reaction. Through several assumptions and via nondimensionalization, we simplify the equations.
    kineticsEnzymesubstratenondimensionalizationmultiple timescales
    Article or Presentation
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    48

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    1990-Winkel_Brian-First-year_calculus_students_as_in-class_consultants
    We demonstrate how we use traditional text book calculus problems to involve first year calculus students as consultants. Students are asked to solve a “real world” problem after interviewing a “guest” client –a printer.
    optimizationcalculusConsultantprinterclient
    Technique Narrative
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    193

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    2-005-LinearizeItAll-TechniqueNarrative
    Linear approximations are often used to simplify nonlinear ordinary differential equations (ODEs) for ease in analysis. The resulting linear approximation produces an ODE where closed form solutions may be obtained.
    computationerrorTorricelli's Lawdata fittinglinear approximation
    Technique Narrative
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    186

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    2-001-NumericalMethodsComparisons-TechniqueNarrative
    This material teaches the basics of numerical methods for first order differential equations by following graphical and numerical approaches. We discuss the order of accuracy of the methods and compare their CPU times.
    Euler's methodimproved Euler's methodRK3 methodsRK4 methodsorder of accuracyabsolute errorCPU time
    Modeling Scenario
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    1-054-GrowthInFarmland-ModelingScenario
    An enriching project developing a model from data with missing temporal information is described. Students fit functions to the data that leads to the creation of a differential equations model, which they then are required to analyze in multiple...
    fittingfarmfarmland