Understanding the time-varying magnetic field within a fusion device is critical for the successful design and construction of clean-burning fusion power plants. Poincaré analysis provides a powerful method for the analysis and visualization of magnetic field lines in fusion devices. Current state-of-the-art relies on manually and iteratively generating Poincaré plots from simulation data. Using Poincaré plots in deep analysis is very time consuming because Poincaré plots can be very computationally expensive, especially for a time-varying simulation with thousands of time steps. Further, the visualization results are so complex that only expert users know how to explore, interpret, and control. In addition, collaboration is hampered due to the high barrier to entry. To this end, we contribute Fugent, a reinforcement learning-based agent capable of recommending and evaluating the importance of exploration regions based on training data captured from historic expert user usage. Using Fugent, we show that important regions can be identified and recommended for further exploration. Fugent is open source.

The rapid advancement in scientific simulations and experimental facilities has resulted in the generation of vast amounts of data at unprecedented scales. The analysis and visualization of large amounts of data is a challenge in and of itself, but the requirements for timeliness significantly magnify these difficulties. Near real-time visualization is critical to monitor and analyze the data produced by these large facilities, but current production tools are not well-suited to these requirements. In this position paper, we share our perspective on some of the challenges, and thus, opportunities for research that stand in the way of near-real-time visualization of large scientific data.

Many scientific communities today have community datasets that are continuously created, curated, and maintained for community use. Such datasets are often hosted and shared through cloud-based data repositories. In this work, we propose a lightweight and affordable visualization cloud service that can be deployed as a companion service of a community dataset. Our target visualization use case is parallel flow visualization, which is crucial for understanding planet-scale phenomena such as the Earth’s atmosphere and ocean. As a core research topic of scientific visualization, parallel flow visualization typically uses HPC computing platforms. It is complex to implement with scalability, deploy with efficiency, and is often considered an advanced form of scientific visualization. Because of the heterogeneous nature of cloud platforms, in this work, we use a swarm-based parallel design to replace traditional HPC designs that assume homogeneity and rely upon conventional methods such as Message Passing Interface (MPI). This design enables interactive visualization of large flow fields in a way that is lightweight, efficient and easily deployable as a cloud service. We demonstrate our proposed system using NOAA’s NCEP ensemble data, which captures turbulent planet-scale atmospheric flows in observed forms, as well as in forecast forms for varying time scales. We evaluate the performance and efficacies of our system on Amazon Web Services (AWS) for three use cases, where remote users can use their laptops to (i) interactively explore global atmospheric flow patterns in general, (ii) to specifically compare how a forecast is different from the observation, and (iii) to explore flow patterns in a typical information visualization dashboard.