Blending liquids

60 citations recorded by the source corpus.

How researchers see this paper

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Latent Space Physics: Towards Learning the Temporal Evolution of Fluid Flow (2018, methodology): “work, as this goal would lead to substantially different approaches. Previous works have attempted generating advected quantities directly, e.g., by using space-time deformations [Prantl et al .2017; Raveendran et al 2014], but we will focus on simulation algorithms that explicitly compute transport”
Generating Liquid Simulations with Deformation-aware Neural Networks (2017, methodology): “Alternative methods have been proposed to work with 4D simulation data [34, 38], however, without being able to capture larger spaces of physical behavior.”
Data-driven fluid simulations using regression forests (2015, methodology): “Another data-driven approach [Raveendran et al. 2014] aimed to generate a large number of fluid simulations by interpolating existing preprocessed simulations.”
Data‐driven projection method in fluid simulation (2016, unknown): “Another common data-driven idea is interpolation [15].”
Fluid directed rigid body control using deep reinforcement learning (2018, methodology): “Besides keyframe-based methods, there are other techniques [Bonev et al. 2017; Raveendran et al. 2014; Thuerey 2016] that synthesize fluid animations by interpolating existing animation data.”