Generalized Additive Models on Test Science Research Document Library https://research.testscience.org/keywords/generalized-additive-models/ Recent content in Generalized Additive Models on Test Science Research Document Library Hugo -- 0.129.0 en-us Institute for Defense Analyses Sat, 01 Jan 2022 00:00:00 +0000 Metamodeling Techniques for Verification and Validation of Modeling and Simulation Data https://research.testscience.org/post/2022-metamodeling-techniques-for-verification-and-validation-of-modeling-and-simulation-data/ Sat, 01 Jan 2022 00:00:00 +0000 https://research.testscience.org/post/2022-metamodeling-techniques-for-verification-and-validation-of-modeling-and-simulation-data/ Modeling and simulation (M&S) outputs help the Director, Operational Test and Evaluation (DOT&E) assess the effectiveness, survivability, lethality, and suitability of systems. To use M&S outputs, DOT&E needs models and simulators to be sufficiently verified and validated. The purpose of this paper is to improve the state of verification and validation by recommending and demonstrating a set of statistical techniques—metamodels, also called statistical emulators—to the M&S community. The paper expands on DOT&E’s existing guidance about metamodel usage by creating methodological recommendations the M&S community could apply to its activities.

Modeling and simulation (M&S) outputs help the Director, Operational Test and Evaluation (DOT&E) assess the effectiveness, survivability, lethality, and suitability of systems. To use M&S outputs, DOT&E needs models and simulators to be sufficiently verified and validated. The purpose of this paper is to improve the state of verification and validation by recommending and demonstrating a set of statistical techniques—metamodels, also called statistical emulators—to the M&S community.

The paper expands on DOT&E’s existing guidance about metamodel usage by creating methodological recommendations the M&S community could apply to its activities. For a deterministic, discrete response variable, we recommend using a nearest neighbor or decision tree model. For a deterministic, continuous response variable, we recommend Gaussian process interpolation. For a stochastic response variable, we recommend a generalized additive model. We also present a set of techniques that testers can use to assess the adequacy of their metamodels. We conclude with a notional example that demonstrates the recommended techniques.

Suggested Citation

Haman, John T, and Curtis G Miller. Metamodeling Techniques for Verification and Validation of Modeling and Simulation Data. IDA Paper P-33230. Alexandria, VA: Institute for Defense Analyses, 2022.

Slides:

Paper:

]]>