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  1. parameterestimation x
  1. 3-150-T-ItsABlastFurnace

    13 Aug 2019 | Modeling Scenarios

    This project uses the steady-state heat equation to model the temperature distribution in an industrial furnace used for metal production, for example, a blast furnace.The heat flow is assumed to be steady-state, so that only an elementary ordinary differential equation (ODE) is needed, and...

  2. 3-150-S-ItsABlastFurnace

    13 Aug 2019 | Modeling Scenarios

    This project uses the steady-state heat equation to model the temperature distribution in an industrial furnace used for metal production, for example, a blast furnace.The heat flow is assumed to be steady-state, so that only an elementary ordinary differential equation (ODE) is needed, and...

  3. 3-130-T-MatterOfSomeGravity

    07 Aug 2019 | Modeling Scenarios

    This project introduces the concept of an inverse problem (or parameter estimation), in the context of the simple linearized pendulum ordinary differential equation. The theme of the project is that one often has a physical model for a system but in which the model contains unknown parameters or...

  4. 3-130-S-MatterOfSomeGravity

    07 Aug 2019 | Modeling Scenarios

    This project introduces the concept of an inverse problem (or parameter estimation), in the context of the simple linearized pendulum ordinary differential equation. The theme of the project is that one often has a physical model for a system but in which the model contains unknown parameters or...

  5. 1-107-S-ClothDry

    27 Apr 2019 | Modeling Scenarios

    We build a mathematical model for the rate at which drying takes place in a cloth wet with water while hanging in air. A model can be based on underlying physical principles. Such a model is called an analytic model. Or your model could be based on observations and reasoned terms in your...

  6. 1-107-T-ClothDry

    27 Apr 2019 | Modeling Scenarios

    We build a mathematical model for the rate at which drying takes place in a cloth wet with water while hanging in air. A model can be based on underlying physical principles. Such a model is called an analytic model. Or your model could be based on observations and reasoned terms in your...

  7. 1-125-T-DiceyPopulations

    04 Mar 2019 | Modeling Scenarios

    We offer students an opportunity to generate unique data for their team on a death and immigration model using 12 and 20 sided dice and then pass on the data to another student team for analysis with a model they built. The key is to recover the parameters and try to explain how the simulation...

  8. 1-125-S-DiceyPopulations

    04 Mar 2019 | Modeling Scenarios

    We offer students an opportunity to generate unique data for their team on a death and immigration model using 12 and 20 sided dice and then pass on the data to another student team for analysis with a model they built. The key is to recover the parameters and try to explain how the simulation...

  9. 3-043-S-BallisticModeling-SpongeDart

    20 Nov 2018 | Modeling Scenarios

    The goal of this project is for students to develop, analyze, and compare three different models for the flight of a sponge dart moving under the influences of gravity and air resistance. The first two models are based respectively on the common simplifying assumptions of no air resistance and...

  10. 3-043-T-BallisticModeling-SpongeDart

    20 Nov 2018 | Modeling Scenarios

    The goal of this project is for students to develop, analyze, and compare three different models for the flight of a sponge dart moving under the influences of gravity and air resistance. The first two models are based respectively on the common simplifying assumptions of no air resistance and...

  11. 6-019-T-EnablingEpidemicExploration

    17 Sep 2018 | Modeling Scenarios

    We offer several strategies for estimating parameters in models of epidemics, one using a Michaelis-Menten saturation infected rate.

  12. 6-019-S-EnablingEpidemicExploration

    17 Sep 2018 | Modeling Scenarios

    We offer several strategies for estimating parameters in models of epidemics, one using a Michaelis-Menten saturation infected rate.

  13. 1-053-T-SlimeSpread

    30 Aug 2018 | Modeling Scenarios

    We offer a video showing real time spread of a cylinder of slime and challenge students to build a mathematical model for this phenomenon.

  14. 1-053-S-SlimeSpread

    30 Aug 2018 | Modeling Scenarios

    We offer a video showing real time spread of a cylinder of slime and challenge students to build a mathematical model for this phenomenon.

  15. 1-141-S-M&MGameRevisited

    27 Aug 2018 | Modeling Scenarios

    In this project students will learn to find a probability distribution using the classical M&M game in SIMIODE.

  16. 1-141-T-M&MGameRevisited

    27 Aug 2018 | Modeling Scenarios

    In this project students will learn to find a probability distribution using the classical M&M game offered in SIMIODE.

  17. 6-026-T-IsleRoyaleModeling

    20 May 2018 | Modeling Scenarios

    The primary aim of this project is to draw a connection between differential equations and vector calculus, using population ecology modeling as a vehicle. This setting allows us to also employ multivariable optimization as a means of model fitting and multivariable integration in the context of...

  18. 1-055-T-WaterFallingInCone

    16 Mar 2018 | Modeling Scenarios

    We offer an opportunity to model the height of a falling body of water in a right circular cone (funnel) and to estimate an appropriate parameter based on data collected from a video of the experiment found on YouTube. This is an application of Torricelli's Law.

  19. Two competing populations: simulations with differential equations

    01 Mar 2018 | Articles and Publications

    Abstract: Suppose two populations are competing with each other. How can they then develop? We offer insights with simulation calculations. In the model is the evolution of the biomass of the two populations described with differential equations. A fundamental building block is the logistic...

  20. Wandi Ding - Experience and Lessons Learned from Using SIMIODE Modeling Scenarios

    19 Jan 2018 | Presentations

    Experience and Lessons Learned from Using SIMIODE Modeling ScenariosBy Wandi Ding, Ryan Florida, Jeffrey Summers, Puran Nepal, Middle Tennesse State UniversityA talk given at the AMS Special Session on Modeling in Differential Equations - High School, Two-Year College, Four-Year Institution at...