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Limitations – emulation

Limitations – emulation. Suitability for models. Must be deterministic. If stochastic then noise must be approximately Gaussian distributed with constant variance (nugget). Some models can’t be emulated. Discontinuous response, huge input / output space. No support for spatial models.

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Limitations – emulation

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  1. Limitations – emulation • Suitability for models. • Must be deterministic. • If stochastic then noise must be approximately Gaussian distributed with constant variance (nugget). • Some models can’t be emulated. • Discontinuous response, huge input / output space. • No support for spatial models. • Input variable type support. • Currently only continuous. • Integeris possible but not implemented. • Discrete/categorical support is possible but requires changes to emulation code.

  2. Limitations – Web application • Requires your model to be exposed on the Web. • Only using a supported interface! • Data encoding support. • The models we’ve been using only have double input and output values. • Challenges with complex data types such as O&M.

  3. Limitations – Web application • Web service interface support. • Not all of the WPS specification is supported. • Only some SOAP/WSDL services are compatible. • Includes all processes exposed using the UncertWeb Processing Service framework. • Conditional input specifications. • Cannot specify a design where input X1 > X2, for example.

  4. Summary • Performing sensitivity analysis can help to understand your model / workflow. • Building emulators can greatly speed up multiple model runs required for sensitivity and uncertainty analysis. • We have built open source tools to make this possible in the Model Web. • Source code: http://github.com/itszootime The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement n° [248488].

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