MSEAS Ocean Test Cases
P.F.J. Lermusiaux, P. Lu, M.P. Ueckermann, P.J. Haley, Jr., W.G. Leslie, T. Lolla Massachusetts Institute of Technology
This research sponsored by the 
1: Learning in Flows behind a Cylinder or Island 2: Learning for 2D Biogeochemical Dynamical Systems 3: Feature Extraction in 3D Timedependent Multivariate Ocean Fields (Click on any test case to reach the description) 
1: Learning in Flows behind a Cylinder or Island 



2: Learning for 2D Biogeochemical Dynamical Systems 



This test case 2 is concerned with discovering and learning biologicalphysical dynamics from sparse ocean data and/or approximate model
information. Such learning includes quantitative learning of parameter values, model functionals and model structures, or in other words,
discovering the model equations for the system as well as the initial and boundary conditions. It also involves learning more qualitative
descriptions (but nonetheless scientific) of the system, such as describing dynamical regimes, discovering global properties of the
biological system, determining when and why the biological dynamics suddenly change, and providing locations and periods over which
certain term or process dominate, etc. Examples of terms include: advection, mixing, lightvariation and grazing.
In what follows, we provide a set of examples, with an increasing level of difficulty, that aim to illustrate such applications of learning for biologicalphysical ocean processes. We note that transfer learning is possible from one example to the next, as well as active learning within or across examples. All examples consist of Nutrient, Phytoplankton and Zooplankton (NPZ) dynamics and illustrate the effects of topography and other physical factors on the biogeochemical interactions. For additional information see Ueckermann and Lermusiaux (2010) High Order Schemes for 2D Unsteady Biogeochemical Ocean Models. 

Examples

3: Feature extraction in 3D timedependent multivariate ocean fields 

