Lagrangian Fluid Simulation with Continuous Convolutions

By Benjamin Ummenhofer, Lukas Prantl, Nils Thürey, Vladlen Koltun. 2020

218 citations recorded by the source corpus.

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Citations in the corpus

Learning to Simulate Complex Physics with Graph Networks (2020, methodology): “We also created WATER-3D, a high-resolution 3D water scenario with randomized water position, initial velocity and volume, comparable to Ummenhofer et al. (2020)’s containers of water.”
Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers (2020, background): “, for rigid-body physics in the context of human reasoning [5, 64, 3] or weather predictions [51], particles are also a popular basis for fluid flow problems [33, 61, 48].”
Multipole Graph Neural Operator for Parametric Partial Differential Equations (2020, background): “The ﬁrst parametrizes the solution operator as a deep convolutional neural network (CNN) between ﬁnite-dimensional Euclidean spaces F θ : R n → R n [21, 44, 3, 9, 40].”
Choose a Transformer: Fourier or Galerkin (2021, methodology): “Deep convolutional neural networks (DCNN) are widely applied to learn the solution maps with a ﬁxed discretization size [1, 9, 40, 36, 35, 100, 86].”
Neural Operator: Learning Maps Between Function Spaces (2021, background): “The work Ummenhofer et al. (2020) proposed a continuous convolution network for fluid problems, where off-grid points are sampled and linearly interpolated.”
Visual Grounding of Learned Physical Models (2020, background): “Other researchers have extended the model to particle systems, which showed impressive results in simulating objects made of different materials like rigid objects and fluids (Mrowca et al., 2018; Li et al., 2019a; Ummenhofer et al., 2020; Sanchez-Gonzalez et al., 2020).”
Learning Mesh-Based Simulation with Graph Networks (2020, background): “Various works [22, 38, 33] use graph neural networks (GNNs) [34, 4] to model particle-based granular materials and fluids, as well as glassy dynamics [3].”
Learning to Control PDEs with Differentiable Physics (2020, methodology): “Lagrangian ﬂuid simulation has been tackled with regression forests (Ladicky et al., 2015), graph neural networks (Mrowca et al., 2018; Li et al., 2019), and continuous convolutions (Ummenhofer et al., 2020).”