1,986 research outputs found
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Innovative Approaches to the Numerical Approximation of PDEs
This workshop was about the numerical solution of PDEs for which classical
approaches,
such as the finite element method, are not well suited or need further
(theoretical) underpinnings.
A prominent example of PDEs for which classical methods are not well
suited are PDEs posed in high space dimensions.
New results on low rank tensor approximation for those problems were
presented.
Other presentations dealt with regularity of PDEs, the numerical solution
of PDEs on surfaces,
PDEs of fractional order, numerical solvers for PDEs that converge with
exponential rates, and
the application of deep neural networks for solving PDEs
A Data-Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition
The Koopman operator is a linear but infinite dimensional operator that
governs the evolution of scalar observables defined on the state space of an
autonomous dynamical system, and is a powerful tool for the analysis and
decomposition of nonlinear dynamical systems. In this manuscript, we present a
data driven method for approximating the leading eigenvalues, eigenfunctions,
and modes of the Koopman operator. The method requires a data set of snapshot
pairs and a dictionary of scalar observables, but does not require explicit
governing equations or interaction with a "black box" integrator. We will show
that this approach is, in effect, an extension of Dynamic Mode Decomposition
(DMD), which has been used to approximate the Koopman eigenvalues and modes.
Furthermore, if the data provided to the method are generated by a Markov
process instead of a deterministic dynamical system, the algorithm approximates
the eigenfunctions of the Kolmogorov backward equation, which could be
considered as the "stochastic Koopman operator" [1]. Finally, four illustrative
examples are presented: two that highlight the quantitative performance of the
method when presented with either deterministic or stochastic data, and two
that show potential applications of the Koopman eigenfunctions
Time-stepping and Krylov methods for large-scale instability problems
With the ever increasing computational power available and the development of
high-performances computing, investigating the properties of realistic very
large-scale nonlinear dynamical systems has been become reachable. It must be
noted however that the memory capabilities of computers increase at a slower
rate than their computational capabilities. Consequently, the traditional
matrix-forming approaches wherein the Jacobian matrix of the system considered
is explicitly assembled become rapidly intractable. Over the past two decades,
so-called matrix-free approaches have emerged as an efficient alternative. The
aim of this chapter is thus to provide an overview of well-grounded matrix-free
methods for fixed points computations and linear stability analyses of very
large-scale nonlinear dynamical systems.Comment: To appear in "Computational Modeling of Bifurcations and
Instabilities in Fluid Mechanics", eds. A. Gelfgat, Springe
hp-FEM for Two-component Flows with Applications in Optofluidics
This thesis is concerned with the application of hp-adaptive finite element methods to a mathematical model of immiscible two-component flows. With the aim of simulating the flow processes in microfluidic optical devices based on liquid-liquid interfaces, we couple the time-dependent incompressible Navier-Stokes equations with a level set method to describe the flow of the fluids and the evolution of the interface between them
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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 â in particular, finite element methods, â 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
A FEM for an optimal control problem of fractional powers of elliptic operators
We study solution techniques for a linear-quadratic optimal control problem
involving fractional powers of elliptic operators. These fractional operators
can be realized as the Dirichlet-to-Neumann map for a nonuniformly elliptic
problem posed on a semi-infinite cylinder in one more spatial dimension. Thus,
we consider an equivalent formulation with a nonuniformly elliptic operator as
state equation. The rapid decay of the solution to this problem suggests a
truncation that is suitable for numerical approximation. We discretize the
proposed truncated state equation using first degree tensor product finite
elements on anisotropic meshes. For the control problem we analyze two
approaches: one that is semi-discrete based on the so-called variational
approach, where the control is not discretized, and the other one is fully
discrete via the discretization of the control by piecewise constant functions.
For both approaches, we derive a priori error estimates with respect to the
degrees of freedom. Numerical experiments validate the derived error estimates
and reveal a competitive performance of anisotropic over quasi-uniform
refinement
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