From capture to simulation

Gregson, James; Ihrke, Ivo; Thuerey, Nils; Heidrich, Wolfgang

doi:10.1145/2601097.2601147

Cited by 68 publications

(21 citation statements)

References 43 publications

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“…This negatively influences generated in-between density fields, since they can arbitrarily vary and potentially produce non-physical results. The Tikhonov regularization term [GITH14] favors minimally energetic flows, while a total variation (TV) regularizer enforces continuity inside the fluid domain. These two regularization terms are commonly added by previous methods to the density matching objective, yielding These regularization terms were successfully employed by several works [GITH14, EHT18].…”

Section: Force-based Fluid Controlmentioning

confidence: 99%

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Honey, I Shrunk the Domain: Frequency‐aware Force Field Reduction for Efficient Fluids Optimization

Tang

Azevedo

Cordonnier

et al. 2021

Computer Graphics Forum

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Figure 1: Our method computes an incompressible force field to guide a smoke simulation, such that densities match a user-defined keyframe (pre-defined hand shape). The proposed reduced representation of the force field optimization uses vector potentials and progressive spectral filtering masks, enabling smooth transitions between the underlying physical phenomena and close matching with the target.

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Section: Force-based Fluid Controlmentioning

confidence: 99%

“…These regularization terms were successfully employed by several works [GITH14, EHT18]. However, one often overlooked aspect of Equation (6) is that finding proper weights (α and β) heavily biases the final solution, potentially inducing the convergence of the solution to a local minimum.…”

Section: Force‐based Fluid Controlmentioning

confidence: 99%

Honey, I Shrunk the Domain: Frequency‐aware Force Field Reduction for Efficient Fluids Optimization

Tang

Azevedo

Cordonnier

et al. 2021

Computer Graphics Forum

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show abstract

“…They create a pyramidal structure to refine the optical flow linearly. In some related works [18][19][20], the NavierStokes (N-S) equation is employed. Wang et al [21] also make use of the N-S equation to refine the fluid motion from dense reconstruction.…”

Section: Related Workmentioning

confidence: 99%

Fluid re-simulation based on physically driven model from video

2015

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Customizing a desired naturalistic fluid simulation result from video to obtain similar artistic effect is significant in practice. But art-directed customizing is challengeable due to the chaotic nature of the physics contained in it, and this still remains to be a difficult task in spite of rapid advancements of computer graphics during the last two decades. This paper focuses on the problem of physically based fluid re-simulation which is foundational to customize desired naturalistic simulation result from video example. In the previous achievements, conventional algorithms primarily recover 3D geometry of fluid surface or obtain the velocity of fluid particles in video. However, to launch new derivative results, just geometry and velocity are not enough, and physically based driven models are promising. We present a novel method that is capable of efficiently recovering physically driven model from an existing video. We advocate a new approach to calculate the velocity and non-normalized surface geometry under the constraints of the appearance and dynamic behavior of the example fluid in multi-scale framework, and use the calculated physical properties quickly to recover the fluid-driven model. In particular, to calculate the surface geometry more accurately, we introduce a scale factor between normalized geometry and non-normalized geometry to acquire a more accurate result. We propose a novel recovery algorithm, in which the particle densities of lattice Boltzmann method can be recovered more accurately from matching fluid advection geometry with the calculated non-normalized geometry. Fluid re-simulations with different types of density can be achieved, including constrained particle density, auto-advection density, and enhanced particle density. We demonstrate our results in several challenging scenarios and provide qualitative evaluation to our method. Some applications based on our approach are also demonstrated in the implementation.

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“…25 These positive properties triggered a recent revival of ADMM and similar operator-splitting methods even as alternatives to IPMs, especially in distributed convex optimization with large-scale problems where moderate precision is acceptable. 26 ADMM methods have been used in many fields, with recent and notable examples in computer graphics, 27 computational fluid dynamics, 28 simulation of deformable structures, 29 and contact dynamics. 30,31 In the field of robotics, a sequential unconstrained minimization technique (SUMT) method based on augmented Lagrangian has been discussed in References 32,33, to our knowledge for the first time in a context of nonsmooth dynamics: SUMT leads to an iteration with the same structure of ADMM, for the same type of constraints.…”

Section: Introductionmentioning

confidence: 99%

Solving variational inequalities and cone complementarity problems in nonsmooth dynamics using the alternating direction method of multipliers

Tasora

Mangoni

Benatti

et al. 2021

Numerical Meth Engineering

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This work presents a numerical method for the solution of variational inequalities arising in nonsmooth flexible multibody problems that involve set-valued forces. For the special case of hard frictional contacts, the method solves a second order cone complementarity problem. We ground our algorithm on the Alternating Direction Method of Multipliers (ADMM), an efficient and robust optimization method that draws on few computational primitives. In order to improve computational performance, we reformulated the original ADMM scheme in order to exploit the sparsity of constraint jacobians and we added optimizations such as warm starting and adaptive step scaling. The proposed method can be used in scenarios that pose major difficulties to other methods available in literature for complementarity in contact dynamics, namely when using very stiff finite elements and when simulating articulated mechanisms with odd mass ratios. The method can have applications in the fields of robotics, vehicle dynamics, virtual reality, and multiphysics simulation in general.

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From capture to simulation

Cited by 68 publications

References 43 publications

Honey, I Shrunk the Domain: Frequency‐aware Force Field Reduction for Efficient Fluids Optimization

Honey, I Shrunk the Domain: Frequency‐aware Force Field Reduction for Efficient Fluids Optimization

Fluid re-simulation based on physically driven model from video

Solving variational inequalities and cone complementarity problems in nonsmooth dynamics using the alternating direction method of multipliers

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