Results 1  10
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10
An Efficient Construction of Reduced Deformable Objects
"... Figure 1: Nonlinear simulation of a deformable object with 92 k tets computed at over 120 Hz after about 4 mins of preprocessing. Many efficient computational methods for physical simulation are based on model reduction. We propose new model reduction techniques for the approximation of reduced forc ..."
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Cited by 7 (4 self)
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Figure 1: Nonlinear simulation of a deformable object with 92 k tets computed at over 120 Hz after about 4 mins of preprocessing. Many efficient computational methods for physical simulation are based on model reduction. We propose new model reduction techniques for the approximation of reduced forces and for the construction of reduced shape spaces of deformable objects that accelerate the construction of a reduced dynamical system, increase the accuracy of the approximation, and simplify the implementation of model reduction. Based on the techniques, we introduce schemes for realtime simulation of deformable objects and interactive deformationbased editing of triangle or tet meshes. We demonstrate the effectiveness of the new techniques in different experiments with elastic solids and shells and compare them to alternative approaches.
Nonpolynomial galerkin projection on deforming meshes
 ACM Trans. Graph
, 2013
"... Figure 1: Our method enables reduced simulation of fluid flow around this flying bird over 2000 times faster than the corresponding full simulation and reduced radiosity computation in this architectural scene over 113 times faster than the corresponding full radiosity. This paper extends Galerkin p ..."
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Cited by 7 (2 self)
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Figure 1: Our method enables reduced simulation of fluid flow around this flying bird over 2000 times faster than the corresponding full simulation and reduced radiosity computation in this architectural scene over 113 times faster than the corresponding full radiosity. This paper extends Galerkin projection to a large class of nonpolynomial functions typically encountered in graphics. We demonstrate the broad applicability of our approach by applying it to two strikingly different problems: fluid simulation and radiosity rendering, both using deforming meshes. Standard Galerkin projection cannot efficiently approximate these phenomena. Our approach, by contrast, enables the compact representation and approximation of these complex nonpolynomial systems, including quotients and roots of polynomials. We rely on representing each function to be modelreduced as a composition of tensor products, matrix inversions, and matrix roots. Once a function has been represented in this form, it can be easily modelreduced, and its reduced form can be evaluated with time and memory costs dependent only on the dimension of the reduced space.
Simulating Articulated Subspace SelfContact
"... Figure 1: A hand mesh composed of 458K tetrahedra, running at 5.8 FPS (171 ms), including both selfcontact detection and resolution. Our algorithm accelerates the computation of complex selfcontacts by a factor of 5 × to 52 × over other subspace methods and 166 × to 391× over fullrank simulations ..."
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Cited by 3 (1 self)
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Figure 1: A hand mesh composed of 458K tetrahedra, running at 5.8 FPS (171 ms), including both selfcontact detection and resolution. Our algorithm accelerates the computation of complex selfcontacts by a factor of 5 × to 52 × over other subspace methods and 166 × to 391× over fullrank simulations. Our selfcontact computation never dominates the total time, and takes up at most 46 % of a single frame. We present an efficient new subspace method for simulating the selfcontact of articulated deformable bodies, such as characters. Selfcontact is highly structured in this setting, as the limited space of possible articulations produces a predictable set of coherent collisions. Subspace methods can leverage this coherence, and have been used in the past to accelerate the collision detection stage of contact simulation. We show that these methods can be used to accelerate the entire contact computation, and allow selfcontact to be resolved without looking at all of the contact points. Our analysis of the problem yields a broader insight into the types of nonlinearities that subspace methods can efficiently approximate, and leads us to design a posespace cubature scheme. Our algorithm accelerates selfcontact by up to an order of magnitude over other subspace simulations, and accelerates the overall simulation by two orders of magnitude over fullrank simulations. We demonstrate the simulation of high resolution (100K – 400K elements) meshes in selfcontact at interactive rates (5.8 – 50 FPS).
Enhancements to Modelreduced Fluid Simulation
"... We present several enhancements to modelreduced fluid simulation that allow improved simulation bases and twoway solidfluid coupling. Specifically, we present a basis enrichment scheme that allows us to combine data driven or artistically derived bases with more general analytic bases derived fro ..."
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Cited by 1 (0 self)
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We present several enhancements to modelreduced fluid simulation that allow improved simulation bases and twoway solidfluid coupling. Specifically, we present a basis enrichment scheme that allows us to combine data driven or artistically derived bases with more general analytic bases derived from Laplacian Eigenfunctions. We handle twoway solidfluid coupling in a timesplitting fashion—we alternately timestep the fluid and rigid body simulators, while taking into account the effects of the fluid on the rigid bodies and vice versa. We employ the vortex panel method to handle solidfluid coupling and use dynamic pressure to compute the effect of the fluid on rigid bodies.
ETH Zurich
"... Figure 1: The obtained results using our regression forest method, capable of simulating millions of particles in realtime. Our promising results suggest the applicability of machine learning techniques to physicsbased simulations in timecritical settings, where running time matters more than the ..."
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Figure 1: The obtained results using our regression forest method, capable of simulating millions of particles in realtime. Our promising results suggest the applicability of machine learning techniques to physicsbased simulations in timecritical settings, where running time matters more than the physical exactness. Traditional fluid simulations require large computational resources even for an average sized scene with the main bottleneck being a very small time step size, required to guarantee the stability of the solution. Despite a large progress in parallel computing and efficient algorithms for pressure computation in the recent years, realtime fluid simulations have been possible only under very restricted conditions. In this paper we propose a novel machine learning based approach, that formulates physicsbased fluid simulation as a regression problem, estimating the acceleration of every particle for each frame. We designed a feature vector, directly modelling individual forces and constraints from the NavierStokes equations, giving the method strong generalization properties to reliably predict positions and velocities of particles in a large time step setting on yet unseen test videos. We used a regression forest to approximate the behaviour of particles observed in the large training set of simulations obtained using a traditional solver. Our GPU implementation led to a speedup of one to three orders of magnitude compared to the stateoftheart positionbased fluid solver and runs in realtime for systems with up to 2 million particles.
A Dimensionreduced Pressure Solver for Liquid Simulations
"... Figure 1: Our method can efficiently compute a coarse pressure solve for highresolution liquid simulations while taking into account freesurface boundary conditions. Here, three images of a liquid simulation are shown. The pressure solve uses a resolution (33×25×33) which is 163 times smaller than ..."
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Figure 1: Our method can efficiently compute a coarse pressure solve for highresolution liquid simulations while taking into account freesurface boundary conditions. Here, three images of a liquid simulation are shown. The pressure solve uses a resolution (33×25×33) which is 163 times smaller than the resolution of the surface levelset (513×385×513). The coarse pressure samples for a line along x are illustrated in yellow on the left. This work presents a method for efficiently simplifying the pressure projection step in a liquid simulation. We first devise a straightforward dimension reduction technique that dramatically reduces the cost of solving the pressure projection. Next, we introduce a novel change of basis that satisfies freesurface boundary conditions exactly, regardless of the accuracy of the pressure solve. When combined, these ideas greatly reduce the computational complexity of the pressure solve without compromising free surface boundary conditions at the highest level of detail. Our techniques are easy to parallelize, and they effectively eliminate the computational bottleneck for large liquid simulations.
Basis Enrichment and Solidfluid Coupling for Modelreduced Fluid Simulation
"... We present several enhancements to modelreduced fluid simulation that allow improved simulation bases and twoway solidfluid coupling. Specifically, we present a basis enrichment scheme that allows us to combine data driven or artistically derived bases with more general analytic bases derived fro ..."
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We present several enhancements to modelreduced fluid simulation that allow improved simulation bases and twoway solidfluid coupling. Specifically, we present a basis enrichment scheme that allows us to combine data driven or artistically derived bases with more general analytic bases derived from Laplacian Eigenfunctions. We handle twoway solidfluid coupling in a timesplitting fashion— we alternately timestep the fluid and rigid body simulators, while taking into account the effects of the fluid on the rigid bodies and vice versa. We employ the vortex panel method to handle solidfluid coupling and use dynamic pressure to compute the effect of the fluid on rigid bodies.
SelfRefining
"... Figure 1: A selfrefining liquid control game uses player analytics to guide precomputation to the most visited regions of the liquid’s state space. The game’s quality continuously improves over time, ultimately providing a highquality, interactive experience. Datadriven simulation demands good tr ..."
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Figure 1: A selfrefining liquid control game uses player analytics to guide precomputation to the most visited regions of the liquid’s state space. The game’s quality continuously improves over time, ultimately providing a highquality, interactive experience. Datadriven simulation demands good training data drawn from a vast space of possible simulations. While fully sampling these large spaces is infeasible, we observe that in practical applications, such as gameplay, users explore only a vanishingly small subset of the dynamical state space. In this paper we present a sampling approach that takes advantage of this observation by concentrating precomputation around the states that users are most likely to encounter. We demonstrate our technique in a prototype selfrefining game whose dynamics improve with play, ultimately providing realistically rendered, rich fluid dynamics in real time on a mobile device. Our results show that our analyticsdriven training approach yields lower model error and fewer visual artifacts than a heuristic training strategy.
DataDriven Methods for Interactive Simulation of Complex Phenomena
, 2014
"... duction, crowdsourcing, player models Creating realistic virtual worlds requires fast, detailed physical simulations. Traditional simulation techniques based on discretization in time and space must trade speed for detail. Frequently, this tradeoff results in either coarse, unrealistic simulation, ..."
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duction, crowdsourcing, player models Creating realistic virtual worlds requires fast, detailed physical simulations. Traditional simulation techniques based on discretization in time and space must trade speed for detail. Frequently, this tradeoff results in either coarse, unrealistic simulation, or slowerthanrealtime response. Datadriven simulation techniques avoid this tradeoff by operating on compact representations of simulation state, which can be updated quickly due to their small size. These representations are learned from training simulations that resemble the runtime output we want the simulation to produce. In this thesis, we greatly expand the scope of datadriven simulation in practical applications by answering three important questions. First, how can we reconfigure simulation domains at runtime? While simple forms of datadriven simulation operate in a monolithic fashion, we show how one important datadriven simulation technique can be extended to create modular simulation tiles that can be rearranged at runtime. Second, how can we simulate a wide variety of phenomena? One popular datadriven simulation method, Galerkin projection, only works for simulations with polynomial dynam
Subspace Condensation: Full Space Adaptivity for Subspace Deformations
"... Figure 1: (a) The simulation runs at 16 FPS, entirely within the subspace, 67 ⇥ faster than a full space simulation over the entire mesh. (b) Novel wall collisions begin, activating full space tets, shown in red in the inset. The simulation still runs at 2.1 FPS, a 7.7 ⇥ speedup. (c) Collisions prod ..."
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Figure 1: (a) The simulation runs at 16 FPS, entirely within the subspace, 67 ⇥ faster than a full space simulation over the entire mesh. (b) Novel wall collisions begin, activating full space tets, shown in red in the inset. The simulation still runs at 2.1 FPS, a 7.7 ⇥ speedup. (c) Collisions produce a deformation far outside the basis, and 49 % of the tets are simulated in full space. The step runs at 0.5 FPS; still a 1.9⇥ speedup. (d) The collisions are removed, and the 67 ⇥ speedup returns. Subspace deformable body simulations can be very fast, but can behave unrealistically when behaviors outside the prescribed subspace, such as novel external collisions, are encountered. We address this limitation by presenting a fast, flexible new method that allows full space computation to be activated in the neighborhood of novel events while the rest of the body still computes in a subspace. We achieve this using a method we call subspace condensation, a variant on the classic static condensation precomputation. However, instead of a precomputation, we use the speed of subspace methods to perform the condensation at every frame. This approach allows the full space regions to be specified arbitrarily at runtime, and forms a natural twoway coupling with the subspace regions. While condensation is usually only applicable to linear materials, the speed of our technique enables its application to nonlinear materials as well. We show the effectiveness of our approach by applying it to a variety of articulated character scenarios.