A polynomial particle-in-cell method

Fu, Chuyuan; Guo, Qi; Gast, Theodore; Jiang, Chenfanfu; Teran, Joseph

doi:10.1145/3130800.3130878

Cited by 100 publications

(77 citation statements)

References 54 publications

(57 reference statements)

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“…Jiang et al proposed the affine PIC (APIC) method to alleviate the loss of angular momentum in PIC by augmenting each particle with a locally affine, rather than locally constant, description of the velocity. APIC was recently generalized by augmenting each particle with a more general local polynomial function . Overall, these methods can only reduce the numerical dissipation to a limited extent.…”

Section: Related Workmentioning

confidence: 99%

“…Great efforts have been made to improve the accuracy of advection such as using higher order integration schemes and particle‐grid hybrid framework; however, a particularly problematic artifact of the dissipation in advection is loss of angular momentum. In the recent years, there has been some works that address this particular type of numerical dissipation mainly by considering energy transport, but they can only solve the problem to a limited extent.…”

Section: Introductionmentioning

confidence: 99%

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Synthetic fluid details for the vorticity loss in advection

Zhu

Luo

Ren

et al. 2018

Computer Animation & Virtual

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In this paper, a novel method with good numerical stability is proposed from the perspective of energy preserving to alleviate the numerical dissipations in the advection step of Eulerian fluid simulation. The main idea is to measure the vorticity loss during advection, calculate the lost angular kinetic energy with a proposed scheme, and then synthesize a high-frequency incompressible details field to compensate the lost energy in a way that is consistent with Kolmogorov's theory, which prevents the synthetic details from interfering with the existing fluid flow. The method works independently of the advection scheme and can be easily combined with other advection schemes to enhance the effect. It adds only 5% to 10% of the computational overhead while producing convincing fluid details without changing the overall behavior of the original flow.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Synthetic fluid details for the vorticity loss in advection

Zhu

Luo

Ren

et al. 2018

Computer Animation & Virtual

View full text Add to dashboard Cite

show abstract

“…Our model can be extended to other areas. The key idea of our method can be extended to data-driven methods to simulate other particle systems, such as fluid simulation [9,11] and cloth simulation. If we treat the vertex as the agent in our system and the connection between vertices as the relationship, our framework can also be applied to data-driven body animation [19].…”

Section: Conclusion Limitation and Future Workmentioning

confidence: 99%

Heter-Sim: Heterogeneous Multi-Agent Systems Simulation by Interactive Data-Driven Optimization

Ren

Wei

Xiao

et al. 2021

IEEE Trans. Visual. Comput. Graphics

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Figure 1: Our heterogeneous multi-agent simulation algorithm can be used for scenarios with tens or hundreds of different types of agents sharing a physical space. Pedestrians walking on a street (the first). Cars moving on a twisting road (the second). Traffic including cars and pedestrians (the third). Traffic shown through VR (the fourth). Our approach can generate plausible behaviors at interactive rates on a desktop PC and through VR. ABSTRACTInteractive multi-agent simulation algorithms are used to compute the trajectories and behaviors of different entities in virtual reality scenarios. However, current methods involve considerable parameter tweaking to generate plausible behaviors. We introduce a novel approach (Heter-Sim) that combines physics-based simulation methods with data-driven techniques using an optimization-based formulation. Our approach is general and can simulate heterogeneous agents corresponding to human crowds, traffic, vehicles, or combinations of different agents with varying dynamics. We estimate motion states from real-world datasets that include information about position, velocity, and control direction. Our optimization algorithm considers several constraints, including velocity continuity, collision avoidance, attraction, and direction control. To accelerate the computations, we reduce the search space for both collision avoidance and optimal solution computation. Heter-Sim can simulate tens or hundreds of agents at interactive rates and we compare its accuracy with real-world datasets and prior algorithms. We also perform user studies that evaluate the plausible behaviors generated by our algorithm and a user study that evaluates the plausibility of our algorithm via VR.

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“…Two‐phase fluids have been also simulated via an extension of FLIP [BB12]. Moreover, the affine particle‐in‐cell (APIC) method [JSS*15] as a variant effectively addressed the stability issues of FLIP and the dissipation of the particle‐in‐cell method, and APIC has been further generalized to polynomial representations [FGG*17].…”

Section: Related Workmentioning

confidence: 99%

Liquid Splash Modeling with Neural Networks

Thuerey

2018

Computer Graphics Forum

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This paper proposes a new data‐driven approach to model detailed splashes for liquid simulations with neural networks. Our model learns to generate small‐scale splash detail for the fluid‐implicit‐particle method using training data acquired from physically parametrized, high resolution simulations. We use neural networks to model the regression of splash formation using a classifier together with a velocity modifier. For the velocity modification, we employ a heteroscedastic model. We evaluate our method for different spatial scales, simulation setups, and solvers. Our simulation results demonstrate that our model significantly improves visual fidelity with a large amount of realistic droplet formation and yields splash detail much more efficiently than finer discretizations.

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A polynomial particle-in-cell method

Cited by 100 publications

References 54 publications

Synthetic fluid details for the vorticity loss in advection

Synthetic fluid details for the vorticity loss in advection

Heter-Sim: Heterogeneous Multi-Agent Systems Simulation by Interactive Data-Driven Optimization

Liquid Splash Modeling with Neural Networks

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