From our fundamental AI advancements in theory and computation, we will port our methods to leading grand challenge problems where model complexity, unknown physics, multiscale and multiphysics phenomena dominate. We will then pursue the following areas. Critical for the reduction of methods to practice is a set of application problems where a common task framework (CTF) can be used to evaluate methods. This has been exceptionally successful in the computer vision and speech recognition communities where ML/AI has had transformative impact. Our goal is to provide a comprehensive challenge data set framework for evaluating data-driven methods and their performance across a wide range of tasks for dynamical systems from observational data. We will apply this across a multiple of applications whose objectives are also diverse in nature.