Techniques for Data enhancement

Abstract: Extracting extra information from data can allow for more insight into the interaction between disparate scales. It can also aid in minimising error and decreasing noise in data. While the ability to move data from fine resolutions to coarser resolutions is straight forward utilizing a multi-resolution analysis framework, moving data from a coarse resolution to a finer resolution while reducing errors is more challenging. This relies on combining filtering techniques into the multi-resolution analysis framework. This approach has the further advantage of requiring fewer computations to gain insight into calculations such as for Bohm speed. In this talk, we present methods for data enhancement through multi-resolution analysis and the Smoothness-Increasing Accuracy-Conserving (SIAC) filtering framework. SIAC is known to inherently take advantage of the underlying physics and allow for the full resolution of the approximation and its derivatives in both the physical domain and Fourier signal space. We discuss recent advances and reliance of the approach on the underlying numerical method that generated the data.

numerical analysisoptimization and control

Audience: researchers in the topic

CAM seminar

Series comments: Online streaming via zoom on exceptional cases if requested. Please contact the organizers at the latest Monday 11:45.