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Register a new userOn fast computation of finite-time coherent sets using radial basis functions
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Finite-time coherent sets inhibit mixing over finite times. The most expensive part of the transfer operator approach to detecting coherent sets is the construction of the operator itself. We present a numerical method based on radial basis function collocation and apply it to a recent transfer operator construction that has been designed specifically for purely advective dynamics. The construction is based on a dynamic Laplacian operator and minimises the boundary size of the coherent sets relative to their volume. The main advantage of our new approach is a substantial reduction in the number of Lagrangian trajectories that need to be computed, leading to large speedups in the transfer operator analysis when this computation is costly.
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In this paper we consider two sources of enhancement for the meshfree Lagrangian particle method smoothed particle hydrodynamics (SPH) by improving the accuracy of the particle approximation. Namely, we will consider shape functions constructed using: moving least-squares approximation (MLS); radial basis functions (RBF). Using MLS approximation is appealing because polynomial consistency of the particle approximation can be enforced. RBFs further appeal as they allow one to dispense with the smoothing-length -- the parameter in the SPH method which governs the number of particles within the support of the shape function. Currently, only ad hoc methods for choosing the smoothing-length exist. We ensure that any enhancement retains the conservative and meshfree nature of SPH. In doing so, we derive a new set of variationally-consistent hydrodynamic equations. Finally, we demonstrate the performance of the new equations on the Sod shock tube problem.
Spherical radial basis functions are used to define approximate solutions to strongly elliptic pseudodifferential equations on the unit sphere. These equations arise from geodesy. The approximate solutions are found by the Galerkin and collocation methods. A salient feature of the paper is a {em unified theory} for error analysis of both approximation methods.
We propose a discretization of the optimality principle in dynamic programming based on radial basis functions and Shepards moving least squares approximation method. We prove convergence of the approximate optimal value function to the true one and present several numerical experiments.
Reduced bases have been introduced for the approximation of parametrized PDEs in applications where many online queries are required. Their numerical efficiency for such problems has been theoretically confirmed in cite{BCDDPW,DPW}, where it is shown that the reduced basis space $V_n$ of dimension $n$, constructed by a certain greedy strategy, has approximation error similar to that of the optimal space associated to the Kolmogorov $n$-width of the solution manifold. The greedy construction of the reduced basis space is performed in an offline stage which requires at each step a maximization of the current error over the parameter space. For the purpose of numerical computation, this maximization is performed over a finite {em training set} obtained through a discretization. of the parameter domain. To guarantee a final approximation error $varepsilon$ for the space generated by the greedy algorithm requires in principle that the snapshots associated to this training set constitute an approximation net for the solution manifold with accuracy or order $varepsilon$. Hence, the size of the training set is the $varepsilon$ covering number for $mathcal{M}$ and this covering number typically behaves like $exp(Cvarepsilon^{-1/s})$ for some $C>0$ when the solution manifold has $n$-width decay $O(n^{-s})$. Thus, the shear size of the training set prohibits implementation of the algorithm when $varepsilon$ is small. The main result of this paper shows that, if one is willing to accept results which hold with high probability, rather than with certainty, then for a large class of relevant problems one may replace the fine discretization by a random training set of size polynomial in $varepsilon^{-1}$. Our proof of this fact is established by using inverse inequalities for polynomials in high dimensions.
We describe a new method for computing coherent Lagrangian vortices in two-dimensional flows according to any of the following approaches: black-hole vortices [Haller & Beron-Vera, 2013], objective Eulerian Coherent Structures (OECSs) [Serra & Haller, 2016], material barriers to diffusive transport [Haller et al., 2018, Haller et al., 2019], and constrained diffusion barriers [Haller et al., 2019]. The method builds on ideas developed previously in [Karrasch et al., 2015], but our implementation alleviates a number of shortcomings and allows for the fully automated detection of such vortices on unprecedentedly challenging real-world flow problems, for which specific human interference is absolutely infeasible. Challenges include very large domains and/or parameter spaces. We demonstrate the efficacy of our method in dealing with such challenges on two test cases: first, a parameter study of a turbulent flow, and second, computing material barriers to diffusive transport in the global ocean.
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