This is a python implementation of deGradInfer and incorporates methods from the latest research papers. This is performing parameter estimation in non-linear ordinary differential equation models, where we have noisy observations and are aware of the system of equations but not aware of the parameters, and the task is to infer the parameters and the states given noisy and/or incomplete observations.
Imagine having observations of an apple falling on the moon, and where we know the system of equations, this would module would give you the gravitational constant.
These kind of problems are popular in systems biology where we have some prior knowledge on the system of equations and in some cases we may have some knowledge of the parameters or in some cases we may not be able to observe all the states. For this reason we use a technique called Gaussian Process Gradient Matching.