Jetly, VishrutIbayashi, HikaruNakano, AiichiroSauvage, BasileHasic-Telalovic, Jasminka2022-04-222022-04-222022978-3-03868-171-71017-4656https://doi.org/10.2312/egp.20221003https://diglib.eg.org:443/handle/10.2312/egp20221003We present sharpness-aware minimization (SAM) for fluid dynamics which can efficiently learn the plausible dynamics of liquid splashes. Due to its ability to achieve robust and generalizing solutions, SAM efficiently converges to a parameter set that predicts plausible dynamics of elusive liquid splashes. Our training scheme requires 6 times smaller number of epochs to converge and, 4 times shorter wall-clock time. Our result shows that sharpness of loss function has a close connection to the plausibility of fluid dynamics and suggests further applicability of SAM to machine learning based fluid simulation.Attribution 4.0 International LicenseCCS Concepts: Animation --> Fluid simulation; Methods and Applications --> Machine Learning; Neural Nets ; Optimization; Visualization --> Scientific VisualizationAnimationFluid simulationMethods and ApplicationsMachine LearningNeural NetsOptimizationVisualizationScientific Visualization10.2312/egp.202210037-93 pages