Incorporating Data

MrHyDE is designed to enable data-informed physics-based predictions by combining inference with modeling and simulation. There are several ways to incorporate experimental (or synthetic) data into a simulation, but these can be broken down into two classes:

1. Input data: information that can be defined as parameters, coefficients, forcing functions, etc. This is often used along with the forward analysis mode.
2. Output data: information that the simulation will try to predict and then try to find the parameters that best match the data. This is usually used in PDE-constrained optimization, stochastic inversion, data-assimilation, etc.

Incorporating output data in a simulation is described in the Optimization and Data-Consistent Inversion pages.
We focus the rest of this page on incorporating input data.