247 research outputs found
A Hermite interpolatory subdivision scheme for -quintics on the Powell-Sabin 12-split
In order to construct a -quadratic spline over an arbitrary
triangulation, one can split each triangle into 12 subtriangles, resulting in a
finer triangulation known as the Powell-Sabin 12-split. It has been shown
previously that the corresponding spline surface can be plotted quickly by
means of a Hermite subdivision scheme. In this paper we introduce a nodal
macro-element on the 12-split for the space of quintic splines that are locally
and globally . For quickly evaluating any such spline, a Hermite
subdivision scheme is derived, implemented, and tested in the computer algebra
system Sage. Using the available first derivatives for Phong shading, visually
appealing plots can be generated after just a couple of refinements.Comment: 17 pages, 7 figure
Locally supported, piecewise polynomial biorthogona wavelets on non-uniform meshes
In this paper, biorthogonal wavelets are constructed on non-uniform meshes. Both primal and dual wavelets are explicitly given locally supported, continuous piecewise polynomials. The wavelets generate Riesz bases for the Sobolev spaces H s
for j s j < 3 2 . The wavelets at the primal side span standard Lagrange nite element spaces
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New Discretization Methods for the Numerical Approximation of PDEs
The construction and mathematical analysis of numerical methods for PDEs is a fundamental area of modern applied mathematics. Among the various techniques that have been proposed in the past, some – in particular, finite element methods, – have been exceptionally successful in a range of applications. There are however a number of important challenges that remain, including the optimal adaptive finite element approximation of solutions to transport-dominated diffusion problems, the efficient numerical approximation of parametrized families of PDEs, and the efficient numerical approximation of high-dimensional partial differential equations (that arise from stochastic analysis and statistical physics, for example, in the form of a backward Kolmogorov equation, which, unlike its formal adjoint, the forward Kolmogorov equation, is not in divergence form, and therefore not directly amenable to finite element approximation, even when the spatial dimension is low). In recent years several original and conceptionally new ideas have emerged in order to tackle these open problems.
The goal of this workshop was to discuss and compare a number of novel approaches, to study their potential and applicability, and to formulate the strategic goals and directions of research in this field for the next five years
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Wavelet and Multiscale Methods
Various scientific models demand finer and finer resolutions of relevant features. Paradoxically, increasing computational power serves to even heighten this demand. Namely, the wealth of available data itself becomes a major obstruction. Extracting essential information from complex structures and developing rigorous models to quantify the quality of information leads to tasks that are not tractable by standard numerical techniques. The last decade has seen the emergence of several new computational methodologies to address this situation. Their common features are the nonlinearity of the solution methods as well as the ability of separating solution characteristics living on different length scales. Perhaps the most prominent examples lie in multigrid methods and adaptive grid solvers for partial differential equations. These have substantially advanced the frontiers of computability for certain problem classes in numerical analysis. Other highly visible examples are: regression techniques in nonparametric statistical estimation, the design of universal estimators in the context of mathematical learning theory and machine learning; the investigation of greedy algorithms in complexity theory, compression techniques and encoding in signal and image processing; the solution of global operator equations through the compression of fully populated matrices arising from boundary integral equations with the aid of multipole expansions and hierarchical matrices; attacking problems in high spatial dimensions by sparse grid or hyperbolic wavelet concepts. This workshop proposed to deepen the understanding of the underlying mathematical concepts that drive this new evolution of computation and to promote the exchange of ideas emerging in various disciplines
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Computational Multiscale Methods
Many physical processes in material sciences or geophysics are characterized by inherently complex interactions across a large range of non-separable scales in space and time. The resolution of all features on all scales in a computer simulation easily exceeds today's computing resources by multiple orders of magnitude. The observation and prediction of physical phenomena from multiscale models, hence, requires insightful numerical multiscale techniques to adaptively select relevant scales and effectively represent unresolved scales. This workshop enhanced the development of such methods and the mathematics behind them so that the reliable and efficient numerical simulation of some challenging multiscale problems eventually becomes feasible in high performance computing environments
Numerical Methods for the Chemical Master Equation
The dynamics of biochemical networks can be described by a Markov jump process on a high-dimensional state space, with the corresponding probability distribution being the solution of the Chemical Master Equation (CME). In this thesis, adaptive wavelet methods for the time-dependent and stationary CME, as well as for the approximation of committor probabilities are devised. The methods are illustrated on multi-dimensional models with metastable solutions and large state spaces
Wavelet-based Edge Multiscale Parareal Algorithm for subdiffusion equations with heterogeneous coefficients in a large time domain
We present the Wavelet-based Edge Multiscale Parareal (WEMP) Algorithm,
recently proposed in [Li and Hu, {\it J. Comput. Phys.}, 2021], for efficiently
solving subdiffusion equations with heterogeneous coefficients in long time.
This algorithm combines the benefits of multiscale methods, which can handle
heterogeneity in the spatial domain, and the strength of parareal algorithms
for speeding up time evolution problems when sufficient processors are
available. Our algorithm overcomes the challenge posed by the nonlocality of
the fractional derivative in previous parabolic problem work by constructing an
auxiliary problem on each coarse temporal subdomain to completely uncouple the
temporal variable. We prove the approximation properties of the correction
operator and derive a new summation of exponential to generate a single-step
time stepping scheme, with the number of terms of
independent of the final time, where
is the fine-scale time step size. We establish the convergence rate of our
algorithm in terms of the mesh size in the spatial domain, the level parameter
used in the multiscale method, the coarse-scale time step size, and the
fine-scale time step size. Finally, we present several numerical tests that
demonstrate the effectiveness of our algorithm and validate our theoretical
results.Comment: arXiv admin note: text overlap with arXiv:2003.1044
Cloud-Based Benchmarking of Medical Image Analysis
Medical imagin
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