Exploratory Lagrangian-Based Particle Tracing Using Deep Learning

TL;DR

This paper introduces a deep learning-based particle tracing method for exploring large, time-varying vector fields efficiently by learning Lagrangian flow maps, enabling fast, memory-efficient analysis.

Contribution

It presents a novel neural network approach to predict particle trajectories from Lagrangian flow maps, reducing memory and computation costs compared to traditional methods.

Findings

Requires only 10.5 MB memory for Lagrangian representation

Model loading takes only two seconds for analysis

Parallel inference achieves 2000 pathlines in 1.3 seconds

Abstract

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.