Importance-Driven Exploration of Molecular Dynamics Simulations (DAEV)

  The aim of this thesis is a novel real-time visualization approach for exploring molecular dynamics (MD-)simulations. Through the constantly improving hardware and ever-increasing computing power, MD-simulations are more easily available. Additionally, they consist of hundreds, thousands or even millions of individual simulation frames and are getting more and more detailed. The calculation of such simulations is no longer limited by algorithms or hardware, nevertheless it is still not possible to efficiently explore this huge amount of simulation data, as animated 3D visualization, with ordinary and well established visualization tools. Using current software tools, the exploration of such long simulations takes too much time and due to the complexity of large molecular scenes, the visualizations highly suffer from visual clutter. It is therefore very likely that the user will miss important events.   Therefore, we designed a focus & context approach for MD-simulations that guides the user to the most relevant temporal and spatial events, and it is no longer necessary to explore the simulation in a linear fashion. Our contribution can be divided into the following four topics:   1. Spatial importance through different levels of detail. Depending on the type of research task, different geometrical representations can be selected for both, focus and context elements.   2. Importance driven visibility management through ghosting, to prevent context elements from occluding focus elements.   3. Temporal importance through adaptive fast-forward. The playback speed of the simulation is thereby dependent on a single or a combination of multiple importance functions.   4. Visual declutter of accumulated frames through motion blur, which additionally illustrates the playback speed-up.   Since the very beginning, this work was developed in close cooperation with biochemists from the Loschmidt Laboratories in Brno, Czech Republic. Together, we analyzed different use cases demonstrating the flexibility of our novel focus & context approach