SCA 2024 - Posters

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https://diglib.eg.org/handle/10.2312/3607034

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Browsing SCA 2024 - Posters by Subject "Computing methodologies → Modeling and simulation"

Now showing 1 - 3 of 3
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    Data-driven Friction for Real-time Applications
    (The Eurographics Association, 2024) Nassif, Loïc; Zoubir, O.; Andrews, Sheldon; Kry, Paul G.; Zordan, Victor
    We present a novel data-driven approach for simulating friction between rigid bodies that captures the rich diversity of frictional behaviors that arises due to the complex interactions of micro-asperities of different surfaces. Rather than performing detailed simulations with expensive collision detection, we parameterize our friction model based on aggregate features of pairs of surfaces, such as the distribution of normals from each surfaces, which may be easily computed from a texture-based embedding. Our data-driven model is constructed by conducting real-world planar pushing experiments that capture the friction behavior of many different material pairs, and we then fit this data using a Gaussian process (GP). The trained GP model is then evaluated in a real-time simulation and used to update the limit surface used by the contact solver.
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    Fast Simulation of Viscous Lava Flow Using Green's Functions as a Smoothing Kernel
    (The Eurographics Association, 2024) Kedadry, Yannis; Cordonnier, Guillaume; Zordan, Victor
    We present a novel approach to simulate large-scale lava flow in real-time. We use a depth-averaged model from numerical vulcanology to simplify the problem to 2.5D using a single layer of particle with thickness. Yet, lava flow simulation is challenging due to its strong viscosity which introduces computational instabilities. We solve the associated partial differential equations with approximated Green's functions and observe that this solution acts as a smoothing kernel. We use this kernel to diffuse the velocity based on Smoothed Particle Hydrodynamics. This yields a representation of the velocity that implicitly accounts for horizontal viscosity which is otherwise neglected in standard depth-average models. We demonstrate that our method efficiently simulates large-scale lava flows in real-time.
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    Neural Implicit Reduced Fluid Simulation
    (The Eurographics Association, 2024) Tao, Yuanyuan; Puhachov, Ivan; Nowrouzezahrai, Derek; Kry, Paul; Zordan, Victor
    High-fidelity simulation of fluid dynamics is challenging because of the high dimensional state data needed to capture fine details and the large computational cost associated with advancing the system in time. We present neural implicit reduced fluid simulation (NIRFS), a reduced fluid simulation technique that combines a neural-implicit representation of fluid shapes and a neural ordinary differential equation to model the dynamics of fluid in the reduced latent space. Trajectories for NIRFS can be computed at very little cost in comparison to simulations for generating training data, while preserving many of the fine details. We show that this approach can work well, capturing the shapes and dynamics involved in a variety of scenarios with constrained initial conditions, e.g., droplet-droplet collisions, crown splashes, and fluid slosh in a container. In each scenario, we learn the latent implicit representation of fluid shapes with a deep-network signed distance function, as well as the energy function and parameters of a damped Hamiltonian system, which helps guarantee desirable properties of the latent dynamics. To ensure that latent shape representations form smooth and physically meaningful trajectories, we simultaneously learn the latent representation and dynamics. We evaluate novel simulations for conservation of volume and momentum conservation, discuss design decisions, and demonstrate an application of our method to fluid control.