Major Goals: Our research objectives are two-fold: (1) We will generate high-order FEM appropriate dimensional-reduction feature extraction methods such as vortex cores which can be accomplished as part of an in situ data processing pipeline. (2) Given the exploratory nature inherent in analyzing and visualizing transient phenomena, we will specify the regions of interest in an in situ fashion within a simulation field based upon the visualization objective, extract and transmit relevant high-order FEM modal information to our visualization system, and then reconstruct the visualization features of interest. Accomplishments: Since the start of the grant, we have focused on feature detection in high-order fields. This has involved five main focus areas: 1) implementing and understanding how line-SIAC (L-SIAC) filters can be used to increase smoothness in the numerical solution prior to rendering, without compromising the simulation results. 2) Updating the L-SIAC filter to accommodate the types of meshes commonly encountered in the bulk of engineering scenarios (which would be meshes that are isotropic and/or mildly anisotropic). 3) Accelerating L-SIAC filtering for these applications. 4) Use of the L-SIAC filter as a preprocessing tool prior to topological analysis. 5) The creation of a new L-SIAC filter - the Non-Uniform Knot L-SIAC Filter (NUK L-SIAC Filter) to allow us to handle strongly anisotropic meshes as encountered in adaptive mesh refinement scenarios. All five areas are summarized in this final report.