A modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations
A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, and more for ODEs, SDEs, DDEs, DAEs, etc.
Currently, values for the llh, gradient, the computed trajectories of states and observables, or the sensitivities are checked in unit tests.
Unfortunately, some bugs, such as incorrect Jacobians or switched minus signs in the Newton solver, will not necessarily affect those quantities. However, they will substantially impact solver performance, by causing way too many steps to be taken. Hence, w