Data-driven fluid simulations using regression forests

By Lubor Ladicky, Sohyeon Jeong, Barbara Solenthaler, Marc Pollefeys, Markus H. Gross. 2015

296 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, Numerical Methods & Scientific Computing, AI for Physical Sciences

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Citations in the corpus

Toward Causal Representation Learning (2021, background): “One interesting aspect is physics simulation with neural networks [94], which can substantially increase the efﬁciency of hand-engineered simulators [104], [143], [211], [265], [269].”
Learning to Simulate Complex Physics with Graph Networks (2020, background): “, (Ladickỳ et al., 2015; Wiewel et al., 2019) learn parts of a fluid simulator for faster prediction.”
Latent Space Physics: Towards Learning the Temporal Evolution of Fluid Flow (2018, methodology): “…learning methods are highly competitive alternatives to traditional meth-c ods, e.g., for computing local interactions of particle based liquids [LJS ∗ 15], to perform divergence free projections for a single point in time [TSSP16], or for adversarial training of high resolution ﬂows [XFCT18],…”
Visual Interaction Networks: Learning a Physics Simulator from Video (2017, background): “Ladicky et al. [15] proposed a learned model for simulating ﬂuid dynamics based on regression forests.”
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems (2020, unknown): “1) [266] [10] [219] [158] [140] [182] [203] [76] [86] [130] [174] [204] [231] [313] [121] [125] [187] [92] [306] [180] [298] [184] [267] [39] [247] [133] [130] [231] [298] [173] [66] [24] [255] [12] [17] [203] [204] [213] [239] [125] [281] [214] [176] [249] [310] [309] [311] [245] [78] [266] [297] [”
From Point Clouds to Mesh Using Regression (2017, background): “However, due to their large computational requirements to train large 3D models [21] and the slow evaluation, we decided to use regression forests [3], which were already successfully applied to similar context-based learning problems [28, 15].”
Explainable Machine Learning for Scientific Insights and Discoveries (2019, background): “[2017] and Jeong et al. [2015] use physics-informed approaches for applications such as fluid simulations based on the incompressible Navier-Stokes equations, where physicsbased losses are introduced to achieve plausible results.”
Deep Fluids: A Generative Network for Parameterized Fluid Simulations (2018, methodology): “In fluid simulation, machine learning techniques have been used to replace [Ladický et al. 2015], speed up [Tompson et al.”
Interaction Networks for Learning about Objects, Relations and Physics (2016, background): “Ladický et al. [14] recently used regression forests to learn ﬂuid dynamics.”
Learning Mesh-Based Simulation with Graph Networks (2020, methodology): “[20] use random forests to speed up liquid simulations.”
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).”
Integrating Physics-Based Modeling with Machine Learning: A Survey (2020, unknown): “Improve prediction beyond physical model [287] [35] [102] [104] [271] [247] [4] [199] [139][128] [160] [183] [66] [74] [119] [153] [182] [213] [295] [109] [113] [165] [79] [285] [158] [276] [162] [248] [31] [230] [239] [114] [119] [213] [276] [152] [56] [17] [238] [6] [11] [183] [182] [193] [196] [2”
Deep learning for physical processes: incorporating prior scientific knowledge (2017, methodology): “, 2017] and [Ladický et al., 2015] propose to use regressors for simulating fluid and smoke animation.”
Informed Machine Learning – A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems (2019, background): “While there are many papers in this category, we want to highlight one that integrates prior knowledge as an independent, second source of information by constructing a specific feature vector that directly models physical properties and constraints [44].”
Generating Liquid Simulations with Deformation-aware Neural Networks (2017, methodology): “Our model shares the goal to learn flow physics based on examples with other methods [23, 39, 9], but in contrast to these we focus on 4D volumes of physics data, instead of localized windows at single instances of time.”