  Most common mechanistic models are traditionally presented in mathematical
forms to explain a given physical phenomenon. Machine learning algorithms, on
the other hand, provide a mechanism to map the input data to output without
explicitly describing the underlying physical process that generated the data.
We propose a Data-based Physics Discovery (DPD) framework for automatic
discovery of governing equations from observed data. Without a prior definition
of the model structure, first a free-form of the equation is discovered, and
then calibrated and validated against the available data. In addition to the
observed data, the DPD framework can utilize available prior physical models,
and domain expert feedback. When prior models are available, the DPD framework
can discover an additive or multiplicative correction term represented
symbolically. The correction term can be a function of the existing input
variable to the prior model, or a newly introduced variable. In case a prior
model is not available, the DPD framework discovers a new data-based standalone
model governing the observations. We demonstrate the performance of the
proposed framework on a real-world application in the aerospace industry.
