Scientific Discovery of Dynamic Models Based on Scale-type Constraints

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This paper proposes a novel approach to discover dynamic laws and models represented by simultaneous time differential equations including hidden states from time series data measured in an objective process. This task has not been addressed in the past work though it is essentially important in scientific discovery since any behaviors of objective processes emerge in time evolution. The promising performance of the proposed approach is demonstrated through the analysis of synthetic data.

This paper proposes a novel approach to discover dynamic laws and models represented by simultaneous time differential equations including hidden states from time series data measured in an objective process. This task has not been addressed in the past work though it is essentially important in scientific discovery since any behaviors of objective processes emerge in time evolution. The promising performance of the proposed approach is demonstrated through the analysis of synthetic data.

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