Experimental Design for Symbolic Model Discovery
A method for optimal design of experiments for joint model selection and parametrization determination of a symbolic mathematical model includes: determining a prediction value for a given inquiry data point, functional form and parameterization for conducting an experiment relating to a system unde...
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| Format | Patent |
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| Language | English |
| Published |
22.04.2020
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| Online Access | Get full text |
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| Summary: | A method for optimal design of experiments for joint model selection and parametrization determination of a symbolic mathematical model includes: determining a prediction value for a given inquiry data point, functional form and parameterization for conducting an experiment relating to a system under investigation; assuming a set of input-output data pairs as a starting point in a model discovery process relating to the system under investigation; performing discovery of symbolic models minimizing complexity for a bounded misfit, or minimizing a misfit measure, subject to bounded complexity; determining a new data point through optimal experimental design that informs best as for the underlying symbolic models; and updating a posterior distribution, given results of the experiment relating to the system under investigation for the determined new data point to enable informed assessment among a plurality of functional forms and parameterizations. An apparatus configured to perform the method is also provided. |
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