The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Ryoichi Ando - One of the best experts on this subject based on the ideXlab platform.
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spatially adaptive long term semi lagrangian method for accurate velocity advection
Computational Visual Media, 2018Co-Authors: Takahiro Sato, Christopher Batty, Takeo Igarashi, Ryoichi AndoAbstract:We introduce a new advection scheme for fluid animation. Our main contribution is the use of long-term temporal changes in pressure to extend the commonly used semi-Lagrangian scheme further back along the Time Axis. Our algorithm starts by tracing sample points along a trajectory following the velocity field backwards in Time for many steps. During this backtracing process, the pressure gradient along the path is integrated to correct the velocity of the current Time step. We show that our method effectively suppresses numerical diffusion, retains small-scale vorticity, and provides better long-term kinetic energy preservation.
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a long term semi lagrangian method for accurate velocity advection
International Conference on Computer Graphics and Interactive Techniques, 2017Co-Authors: Takahiro Sato, Christopher Batty, Takeo Igarashi, Ryoichi AndoAbstract:We introduce a new advection scheme for fluid animation. Our main contribution is the use of long-term temporal changes in pressure to extend the commonly used semi-Lagrangian scheme further back along the Time Axis. Our algorithm starts by tracing sample points along a trajectory following the velocity field backwards in Time for many steps. During this backtracing process, the pressure gradient along the path is integrated to correct the velocity of the current Time step. We show that our method effectively suppresses numerical diffusion, retains small-scale vorticity, and provides better long-term kinetic energy preservation.
Takahiro Sato - One of the best experts on this subject based on the ideXlab platform.
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spatially adaptive long term semi lagrangian method for accurate velocity advection
Computational Visual Media, 2018Co-Authors: Takahiro Sato, Christopher Batty, Takeo Igarashi, Ryoichi AndoAbstract:We introduce a new advection scheme for fluid animation. Our main contribution is the use of long-term temporal changes in pressure to extend the commonly used semi-Lagrangian scheme further back along the Time Axis. Our algorithm starts by tracing sample points along a trajectory following the velocity field backwards in Time for many steps. During this backtracing process, the pressure gradient along the path is integrated to correct the velocity of the current Time step. We show that our method effectively suppresses numerical diffusion, retains small-scale vorticity, and provides better long-term kinetic energy preservation.
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a long term semi lagrangian method for accurate velocity advection
International Conference on Computer Graphics and Interactive Techniques, 2017Co-Authors: Takahiro Sato, Christopher Batty, Takeo Igarashi, Ryoichi AndoAbstract:We introduce a new advection scheme for fluid animation. Our main contribution is the use of long-term temporal changes in pressure to extend the commonly used semi-Lagrangian scheme further back along the Time Axis. Our algorithm starts by tracing sample points along a trajectory following the velocity field backwards in Time for many steps. During this backtracing process, the pressure gradient along the path is integrated to correct the velocity of the current Time step. We show that our method effectively suppresses numerical diffusion, retains small-scale vorticity, and provides better long-term kinetic energy preservation.
Christopher Batty - One of the best experts on this subject based on the ideXlab platform.
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spatially adaptive long term semi lagrangian method for accurate velocity advection
Computational Visual Media, 2018Co-Authors: Takahiro Sato, Christopher Batty, Takeo Igarashi, Ryoichi AndoAbstract:We introduce a new advection scheme for fluid animation. Our main contribution is the use of long-term temporal changes in pressure to extend the commonly used semi-Lagrangian scheme further back along the Time Axis. Our algorithm starts by tracing sample points along a trajectory following the velocity field backwards in Time for many steps. During this backtracing process, the pressure gradient along the path is integrated to correct the velocity of the current Time step. We show that our method effectively suppresses numerical diffusion, retains small-scale vorticity, and provides better long-term kinetic energy preservation.
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a long term semi lagrangian method for accurate velocity advection
International Conference on Computer Graphics and Interactive Techniques, 2017Co-Authors: Takahiro Sato, Christopher Batty, Takeo Igarashi, Ryoichi AndoAbstract:We introduce a new advection scheme for fluid animation. Our main contribution is the use of long-term temporal changes in pressure to extend the commonly used semi-Lagrangian scheme further back along the Time Axis. Our algorithm starts by tracing sample points along a trajectory following the velocity field backwards in Time for many steps. During this backtracing process, the pressure gradient along the path is integrated to correct the velocity of the current Time step. We show that our method effectively suppresses numerical diffusion, retains small-scale vorticity, and provides better long-term kinetic energy preservation.
Takeo Igarashi - One of the best experts on this subject based on the ideXlab platform.
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spatially adaptive long term semi lagrangian method for accurate velocity advection
Computational Visual Media, 2018Co-Authors: Takahiro Sato, Christopher Batty, Takeo Igarashi, Ryoichi AndoAbstract:We introduce a new advection scheme for fluid animation. Our main contribution is the use of long-term temporal changes in pressure to extend the commonly used semi-Lagrangian scheme further back along the Time Axis. Our algorithm starts by tracing sample points along a trajectory following the velocity field backwards in Time for many steps. During this backtracing process, the pressure gradient along the path is integrated to correct the velocity of the current Time step. We show that our method effectively suppresses numerical diffusion, retains small-scale vorticity, and provides better long-term kinetic energy preservation.
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a long term semi lagrangian method for accurate velocity advection
International Conference on Computer Graphics and Interactive Techniques, 2017Co-Authors: Takahiro Sato, Christopher Batty, Takeo Igarashi, Ryoichi AndoAbstract:We introduce a new advection scheme for fluid animation. Our main contribution is the use of long-term temporal changes in pressure to extend the commonly used semi-Lagrangian scheme further back along the Time Axis. Our algorithm starts by tracing sample points along a trajectory following the velocity field backwards in Time for many steps. During this backtracing process, the pressure gradient along the path is integrated to correct the velocity of the current Time step. We show that our method effectively suppresses numerical diffusion, retains small-scale vorticity, and provides better long-term kinetic energy preservation.
Jianxin Xu - One of the best experts on this subject based on the ideXlab platform.
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technical communique adaptive ilc for a class of discrete Time systems with iteration varying trajectory and random initial condition
Automatica, 2008Co-Authors: Jianxin XuAbstract:In this work we present a discrete-Time adaptive iterative learning control (AILC) scheme to deal with systems with Time-varying parametric uncertainties. Using the analogy between the discrete-Time Axis and the iterative learning Axis, the new adaptive ILC can incorporate a Recursive Least Squares (RLS) algorithm, hence the learning gain can be tuned iteratively along the learning Axis and pointwisely along the Time Axis. When the initial states are random and the reference trajectory is iteration-varying, the new AILC can achieve the pointwise convergence over a finite Time interval asymptotically along the iterative learning Axis.
