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Modelform and predictive uncertainty quantification in linear aeroelasticity Christian T. Nitschke, Paola Cinnella, Didier Lucor, JeanCamille ChassaingTo cite this version: Christian T. Nitschke,

How to fill out model-form and predictive uncertainty

How to fill out model-form and predictive uncertainty

1. Start by collecting relevant data for the model you are creating.
2. Define the parameters and variables that will be included in the model-form.
3. Write down the mathematical equations that describe the relationships between the variables.
4. Specify the assumptions of your model and the context in which it operates.
5. Organize the model into a structured format, such as tables or matrices, as needed.
6. Calculate the predictions made by the model using the defined equations and input data.
7. Assess the predictive uncertainty by determining the range of possible outcomes and confidence intervals.
8. Document the sources of uncertainty, such as measurement errors or variability in data.
9. Review and refine the model-form based on results and feedback, ensuring clarity and accuracy.

Who needs model-form and predictive uncertainty?

1. Researchers who require accurate modeling for their scientific studies.
2. Analysts in finance and economics to predict market trends and uncertainties.
3. Engineers and scientists in various fields for risk assessments and decision-making.
4. Policy-makers and stakeholders needing robust predictions for planning and resource allocation.
5. Businesses aiming to optimize processes and strategies by understanding potential outcomes.

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What is model-form and predictive uncertainty?

Model-form uncertainty refers to the uncertainty arising from the choice of the model used to represent a system, while predictive uncertainty relates to the uncertainty associated with the predictions made by the model due to various factors, including data limitations and inherent variability.

Who is required to file model-form and predictive uncertainty?

Organizations or individuals that utilize models for decision-making, particularly in regulated industries such as finance and environmental science, are typically required to file model-form and predictive uncertainty.

How to fill out model-form and predictive uncertainty?

To fill out the model-form and predictive uncertainty, one should identify the model used, document assumptions, quantify uncertainties, and provide relevant data and methods used to derive the predictions, ensuring clarity and accuracy.

What is the purpose of model-form and predictive uncertainty?

The purpose of model-form and predictive uncertainty is to provide transparency about the limitations of models and the reliability of their predictions, facilitating informed decision-making and risk assessment.

What information must be reported on model-form and predictive uncertainty?

The information that must be reported includes details about the model structure, parameters, data sources, assumptions, quantified uncertainties, and the implications of these uncertainties for the predictions made.