Figure 1: Our Panda model runs and responds to external perturbations at interactive rates. Our Michelin model does Kung Fu moves. In this paper we present a physics-based framework for simulation and control of human-like skeleton-driven soft body characters. We couple the skeleton dynamics and the soft body dynamics to en-able two-way interactions between the skeleton, the skin geome-try, and the environment. We propose a novel pose-based plasticity model that extends the corotated linear elasticity model to achieve large skin deformation around joints. We further reconstruct con-trols from reference trajectories captured from human subjects by augmenting a sampling-based algorithm. We demonstrate the ef-fectiveness of our framework by results not attainable with a simple combination of previous methods.

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 real-time simulation of deformable objects and interactive deformation-based 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.

Figure 1: Our method augments hand-crafted character animations such as this sumo wrestler with high-quality secondary motion, using an efficient rig-space simulation method. We present an efficient method for augmenting keyframed charac-ter animations with physically-simulated secondary motion. Our method achieves a performance improvement of one to two orders of magnitude over previous work without compromising on qual-ity. This performance is based on a linearized formulation of rig-space dynamics that uses only rig parameters as degrees of free-dom, a physics-based volumetric skinning method that allows our method to predict the motion of internal vertices solely from de-formations of the surface, as well as a deferred Jacobian update scheme that drastically reduces the number of required rig evalua-tions. We demonstrate the performance of our method by compar-ing it to previous work and showcase its potential on a production-quality character rig.

Figure 1: We propose a new “projection-based ” implicit Euler integrator that supports a large variety of geometric constraints in a single physical simulation framework. In this example, all the elements including building, grass, tree, and clothes (49k DoFs, 43k constraints), are simulated at 3.1ms/iteration using 10 iterations per frame (see also accompanying video). We present a new method for implicit time integration of physical systems. Our approach builds a bridge between nodal Finite Element methods and Position Based Dynamics, leading to a simple, efficient, robust, yet accurate solver that supports many different types of constraints. We propose specially designed energy potentials that can be solved efficiently using an alternating optimization approach. Inspired by continuum mechanics, we derive a set of continuum-based potentials that can be efficiently incorporated within our solver. We demonstrate the generality and robustness of our approach in many different applications ranging from the simulation of solids, cloths, and shells, to example-based simulation. Comparisons to Newton-based and Position Based Dynamics solvers highlight the benefits of our formulation.

Rapidity Distribution Comparisons Between and W+4jets Signals At The LHC Using Simulated Data by

This thesis investigates the feasibility of using reconfigurable computers for scientific applications. We review recent developments in reconfigurable high performance computing. We then present designs and implementation details of various scientific applications that we developed for the SRC-6 reconfig-urable computer. We present performance measurements and analysis of the results obtained. We chose a selection of applications from bioinformatics, physics and financial mathematics, including automatic docking of molecular models into electron density maps, lattice gas fluid dynamics simulations, edge detection in images and Monte Carlo options pricing simulations. We conclude that reconfigurable computing is a maturing field that may provide considerable benefit to scientific applications in the future. At present the performance gains offered by reconfigurable computers are not sufficient to justify the expense of the systems, and the software development environ-

Figure 1: Example result of our method: A close-fitting sweater exhibits wrinkles and torsional folds under the effects of gravity and as the underlying torso is twisting. This example used only 12 adaptively chosen basis vectors and ran 18 times faster than a full simulation. We present a new approach to clothing simulation using low-dimensional linear subspaces with temporally adaptive bases. Our method exploits full-space simulation training data in order to construct a pool of low-dimensional bases distributed across pose space. For this purpose, we interpret the simulation data as offsets from a kinematic deformation model that captures the global shape of clothing due to body pose. During subspace simulation, we se-lect low-dimensional sets of basis vectors according to the current pose of the character and the state of its clothing. Thanks to this adaptive basis selection scheme, our method is able to reproduce diverse and detailed folding patterns with only a few basis vectors. Our experiments demonstrate the feasibility of subspace clothing simulation and indicate its potential in terms of quality and compu-tational efficiency.

Figure 1: Only using a single set of parameters, our example-based method can accurately rig various models such as quadrupled animals (a), humans (b, c), and highly deformable models (d). Our method can even generate bone structures for challenging parts, such as the mouth and the two ears of the cat (a), the skirt (b), and the elastic cow model (d). Our method is robust: using only 9 frames, it can generate a skeleton with 28 bones for the cat model (a); note that even though the given example poses of the cat model have asymmetric poses, it still can generate an almost symmetric skeleton without imposing any symmetry constraints. We introduce an example-based rigging approach to automati-cally generate linear blend skinning models with skeletal structure. Based on a set of example poses, our approach can output its skele-ton, joint positions, linear blend skinning weights, and correspond-ing bone transformations. The output can be directly used to set up skeleton-based animation in various 3D modeling and anima-tion software as well as game engines. Specifically, we formu-late the solving of a linear blend skinning model with a skeleton as an optimization with joint constraints and weight smoothness regularization, and solve it using an iterative rigging algorithm that (i) alternatively updates skinning weights, joint locations, and bone transformations, and (ii) automatically prunes redundant bones that can be generated by an over-estimated bone initialization. Due to the automatic redundant bone pruning, our approach is more robust than existing example-based rigging approaches. Furthermore, in terms of rigging accuracy, even with a single set of parameters, our approach can soundly outperform state of the art methods on var-ious types of experimental datasets including humans, quadrupled animals, and highly deformable models.

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Mechanical and superconducting properties of nanosize