Exploratory Lagrangian-Based Particle Tracing Using Deep Learning
This addresses the problem of interactive exploration of large vector fields for scientific visualization researchers, though it appears incremental as it builds on existing Lagrangian representation approaches.
The paper tackles the challenge of analyzing large time-varying vector fields from fluid dynamics simulations by developing a deep neural network-based particle tracing method that learns from Lagrangian flow maps. The method achieves a fixed memory footprint of 10.5 MB, loads in 2 seconds for analysis, and infers 100 locations for 2,000 pathlines in 1.3 seconds on a GPU.
Time-varying vector fields produced by computational fluid dynamics simulations are often prohibitively large and pose challenges for accurate interactive analysis and exploration. To address these challenges, reduced Lagrangian representations have been increasingly researched as a means to improve scientific time-varying vector field exploration capabilities. This paper presents a novel deep neural network-based particle tracing method to explore time-varying vector fields represented by Lagrangian flow maps. In our workflow, in situ processing is first utilized to extract Lagrangian flow maps, and deep neural networks then use the extracted data to learn flow field behavior. Using a trained model to predict new particle trajectories offers a fixed small memory footprint and fast inference. To demonstrate and evaluate the proposed method, we perform an in-depth study of performance using a well-known analytical data set, the Double Gyre. Our study considers two flow map extraction strategies as well as the impact of the number of training samples and integration durations on efficacy, evaluates multiple sampling options for training and testing and informs hyperparameter settings. Overall, we find our method requires a fixed memory footprint of 10.5 MB to encode a Lagrangian representation of a time-varying vector field while maintaining accuracy. For post hoc analysis, loading the trained model costs only two seconds, significantly reducing the burden of I/O when reading data for visualization. Moreover, our parallel implementation can infer one hundred locations for each of two thousand new pathlines across the entire temporal resolution in 1.3 seconds using one NVIDIA Titan RTX GPU.