Category: Uncategorized

Zhongyun He, Jesús Pérez, Miguel A. Otaduy

Numerical coarsening methods offer an attractive methodology for fast simulation of objects with high-resolution heterogeneity. However, they rely heavily on preprocessing, and are not suitable when objects undergo dynamic material or topology updates. We present methods that largely accelerate the two main processes of numerical coarsening, namely training data generation and the optimization of coarsening shape functions, and as a result we manage to leverage runtime numerical coarsening under local material updates. To accelerate the generation of training data, we propose a domain-decomposition solver based on substructuring that leverages local factorizations. To accelerate the computation of coarsening shape functions, we propose a decoupled optimization of smoothness and data fitting. We evaluate quantitatively the accuracy and performance of our proposed methods, and we show that they achieve accuracy comparable to the baseline, albeit with speed-ups of orders of magnitude. We also demonstrate our methods on example simulations with local material and topology updates.

Fast Numerical Coarsening with Local Factorizations

Yongxu Jin , Yushan Han , Zhenglin Geng , Joseph Teran , Ronald Fedkiw

We present a novel paradigm for modeling certain types of dynamic simulation in real-time with the aid of neural networks. In order to significantly reduce the requirements on data (especially time-dependent data), as well as decrease generalization error, our approach utilizes a data-driven neural network only to capture quasistatic information (instead of dynamic or time-dependent information). Subsequently, we augment our quasistatic neural network (QNN) inference with a (real-time) dynamic simulation layer. Our key insight is that the dynamic modes lost when using a QNN approximation can be captured with a quite simple (and decoupled) zero-restlength spring model, which can be integrated analytically (as opposed to numerically) and thus has no time-step stability restrictions. Additionally, we demonstrate that the spring constitutive parameters can be robustly learned from a surprisingly small amount of dynamic simulation data. Although we illustrate the efficacy of our approach by considering soft-tissue dynamics on animated human bodies, the paradigm is extensible to many different simulation frameworks.

Analytically Integratable Zero-restlength Springs for Capturing Dynamic Modes unrepresented by Quasistatic Neural Networks

Amir Hossein Rabbani, Jean-Philippe Guertin , Damien Rioux-Lavoie, Arnaud Schoentgen, Kaitai Tong, Alexandre Sirois-Vigneux, Derek Nowrouzezahrai

Poisson equations appear in many graphics settings including, but not limited to, physics-based fluid simulation. Numerical solvers for such problems strike context-specific memory, performance, stability and accuracy trade-offs. We propose a new Poisson filter-based solver that balances between the strengths of spectral and iterative methods. We derive universal Poisson kernels for forward and inverse Poisson problems, leveraging careful adaptive filter truncation to localize their extent, all while maintaining stability and accuracy. Iterative composition of our compact filters improves solver iteration time by orders-of-magnitude compared to optimized linear methods. While motivated by spectral formulations, we overcome important limitations of spectral methods while retaining many of their desirable properties. We focus on the application of our method to high-performance and high-fidelity fluid simulation, but we also demonstrate its broader applicability. We release our source code at https://github.com/Ubisoft-LaForge/CompactPoissonFilters .

Compact Poisson Filters for Fast Fluid Simulation

Lingchen Yang, Byungsoo Kim, Gaspard Zoss, Baran Gözcü, Markus Gross, Barbara Solenthaler

Active soft bodies can affect their shape through an internal actuation mechanism that induces a deformation. Similar to recent work, this paper utilizes a differentiable, quasi-static, and physics-based simulation layer to optimize for actuation signals parameterized by neural networks. Our key contribution is a general and implicit formulation to control active soft bodies by defining a function that enables a continuous mapping from a spatial point in the material space to the actuation value. This property allows us to capture the signal’s dominant frequencies, making the method discretization agnostic and widely applicable. We extend our implicit model to mandible kinematics for the particular case of facial animation and show that we can reliably reproduce facial expressions captured with high-quality capture systems. We apply the method to volumetric soft bodies, human poses, and facial expressions, demonstrating artist-friendly properties, such as simple control over the latent space and resolution invariance at test time.

Implicit Neural Representation for Physics-driven Actuated Soft Bodies

Xuwen Chen, Xingyu Ni, Bo Zhu, Bin Wang, Baoquan Chen

Magnetoelastic thin shells exhibit great potential in realizing versatile functionalities through a broad range of combination of material stiffness, remnant magnetization intensity, and external magnetic stimuli. In this paper, we propose a novel computational method for forward simulation and inverse design of magnetoelastic thin shells. Our system consists of two key components of forward simulation and backward optimization. On the simulation side, we have developed a new continuum mechanics model based on the Kirchhoff–Love thin-shell model to characterize the behaviors of a megnetolelastic thin shell under external magnetic stimuli. Based on this model, we proposed an implicit numerical simulator facilitated by the magnetic energy Hessian to treat the elastic and magnetic stresses within a unified framework, which is versatile to incorporation with other thin shell models. On the optimization side, we have devised a new differentiable simulation framework equipped with an efficient adjoint formula to accommodate various PDE-constraint, inverse design problems of magnetoelastic thin-shell structures, in both static and dynamic settings. It also encompasses applications of magnetoelastic soft robots, functional Origami, artworks, and meta-material designs. We demonstrate the efficacy of our framework by designing and simulating a broad array of magnetoelastic thin-shell objects that manifest complicated interactions between magnetic fields, materials, and control policies.

Simulation and Optimization of Magnetoelastic Thin Shells

• Learning Physics with a Hierarchical Graph Network
• Physically Based Shape Matching
• Fast Numerical Coarsening with Local Factorizations
• Stability Analysis of Explicit MPM
• Wassersplines for Neural Vector-Field Controlled Animation
• Voronoi Filters for Simulation Enrichment
• Differentiable Simulation for Outcome-Driven Orthognathic Surgery Planning
• High-Order Elasticity Interpolants for Microstructure Simulation
• Surface-Only Dynamic Deformables using a Boundary Element Method
• A Second Order Cone Programming Approach for Simulating Biphasic Materials
• A Second-Order Explicit Pressure Projection Method for Eulerian Fluid Simulation

Somehow I seem to have missed making a page for SCA 2021, so here it is!

• Coupling Friction with Visual Appearance
• Volume Preserving Simulation of Soft Tissue with Skin
• Fast Corotated Elastic SPH Solids with Implicit Zero-Energy Mode Control
• Neural UpFlow: A Scene Flow Learning Approach to Increase the Apparent Resolution of Particle-Based Liquids
• Visual Simulation of Soil-Structure Destruction with Seepage Flows
• Particle Merging-and-Splitting (TVCG Talk)

A. Bernardin, E. Coevoet, P.G. Kry, S. Andrews, C. Duriez, and M. Marchal

In this paper, we propose a physics-based model of suction phenomenon to achieve simulation of deformable objects like suction cups. Our model uses a constraint-based formulation to simulate the variations of pressure inside suction cups. The respective internal pressures are represented as pressure constraints which are coupled with anti-interpenetration and friction constraints. Furthermore, our method is able to detect multiple air cavities using information from collision detection. We solve the pressure constraints based on the ideal gas law while considering several cavity states. We test our model with a number of scenarios reflecting a variety of uses, for instance, a spring loaded jumping toy, a manipulator performing a pick and place task, and an octopus tentacle grasping a soda can. We also evaluate the ability of our model to reproduce the physics of suction cups of varying shapes, lifting objects of different masses, and sliding on a slippery surface. The results show promise for various applications such as the simulation in soft robotics and computer animation.

Constraint-based Simulation of Passive Suction Cups

Purvi Goel, Doug L. James

Manually tuning physics-based animation parameters to explore a simulation outcome space or achieve desired motion outcomes can be notoriously tedious. Unfortunately, this problem has motivated many sophisticated and specialized optimization-based methods for fine-grained (keyframe) control, each of which are typically limited to specific animation phenomena, usually complicated, and, unfortunately, not widely used. In this paper, we propose Unified Many-Worlds Browsing (UMWB), a practical method for sample-level control and exploration of arbitrary physics-based animations. Our approach supports browsing of large simulation ensembles of arbitrary animation phenomena by using a unified volumetric WorldPack representation based on spatiotemporally compressed voxel data associated with geometric occupancy and other low-fidelity animation state. Beyond memory reduction, the WorldPack representation also enables unified query support for interactive browsing: it provides fast evaluation of approximate spatiotemporal queries, such as occupancy tests that find ensemble samples (“worlds”) where material is either IN or NOT IN a user-specified spacetime region. The WorldPack representation also supports real-time hardware-accelerated voxel rendering by exploiting the spatially hierarchical and temporal RLE raster data structure to accelerate GPU ray tracing of compressed animations. Our UMWB implementation supports interactive browsing (and offline refinement) of ensembles containing thousands of simulation samples, and fast spatiotemporal queries and ranking. We show UMWB results using a wide variety of different physics-based animation phenomena—not just Jell-O.

Unified Many Worlds Browsing of Arbitrary Physics-Based Animations

Joel Wretborn, Sean Flynn, Alexey Stomakhin

We present a method for enhancing fluid simulations with realistic bubble and foam detail. We treat bubbles as discrete air particles, two-way coupled with a sparse volumetric Euler flow, as first suggested in [Stomakhin et al. 2020]. We elaborate further on their scheme and introduce a bubble inertia correction term for improved convergence. We also show how one can add bubbles to an already existing fluid simulation using our novel guiding technique, which performs local re-simulation of fluid to achieve more interesting bubble dynamics through coupling. As bubbles reach the surface, they are converted into foam and simulated separately. Our foam is discretized with smoothed particle hydrodynamics (SPH), and we replace forces normal to the fluid surface with a fluid surface manifold advection constraint to achieve more robust and stable results. The SPH forces are derived through proper constitutive modeling of an incompressible viscous liquid, and we explain why this choice is appropriate for “wet” types of foam. This allows us to produce believable dynamics from close-up scenarios to large oceans, with just a few parameters that work intuitively across a variety of scales. Additionally, we present relevant research on air entrainment metrics and bubble distributions that have been used in this work.

Guided Bubbles and Wet Foam for Realistic Whitewater Simulation