FLINT: Learning-based Flow Estimation and Temporal Interpolation for Scientific Ensemble Visualization
This addresses the challenge of visualizing scientific ensemble data with missing flow fields, which is incremental as it adapts existing deep learning methods to a new application in scientific visualization.
FLINT tackles the problem of estimating flow fields for 2D+time and 3D+time scientific ensemble data, even when flow information is partially or completely unavailable, and also produces high-quality temporal interpolants between scalar fields.
We present FLINT (learning-based FLow estimation and temporal INTerpolation), a novel deep learning-based approach to estimate flow fields for 2D+time and 3D+time scientific ensemble data. FLINT can flexibly handle different types of scenarios with (1) a flow field being partially available for some members (e.g., omitted due to space constraints) or (2) no flow field being available at all (e.g., because it could not be acquired during an experiment). The design of our architecture allows to flexibly cater to both cases simply by adapting our modular loss functions, effectively treating the different scenarios as flow-supervised and flow-unsupervised problems, respectively (with respect to the presence or absence of ground-truth flow). To the best of our knowledge, FLINT is the first approach to perform flow estimation from scientific ensembles, generating a corresponding flow field for each discrete timestep, even in the absence of original flow information. Additionally, FLINT produces high-quality temporal interpolants between scalar fields. FLINT employs several neural blocks, each featuring several convolutional and deconvolutional layers. We demonstrate performance and accuracy for different usage scenarios with scientific ensembles from both simulations and experiments.