arXiv subjects

Ronald Haynes

Publications and source records attributed to Ronald Haynes.

4 recordsLinked to original sources

arXiv2020

Domain Decomposition Parabolic Monge-Amp\`ere Approach for Fast Generation of Adaptive Moving Meshes

A fast method is presented for adaptive moving mesh generation in multi-dimensions using a domain decomposition parabolic Monge-Amp\`ere approach. The domain decomposition procedure employed here is non-iterative and involves splitting the computational domain into overlapping subdomains. An adaptive mesh on each subdomain is then computed as the image of the solution of the $L^2$ optimal mass transfer problem using a parabolic Monge-Amp\`ere method. The domain decomposition approach allows straightforward implementation for the parallel computation of adaptive meshes which helps to reduce computational time significantly. Results are presented to show the numerical convergence of the domain decomposition solution to the single domain solution. Several numerical experiments are given to demonstrate the performance and efficiency of the proposed method. The numerical results indicate that the domain decomposition parabolic Monge-Amp\`ere method is more efficient than the standard implementation of the parabolic Monge-Amp\`ere method on the whole domain, in particular when computing adaptive meshes in three spatial dimensions.

math.NA↗

arXiv2019

Schwarz solvers and preconditioners for the closest point method

The discretization of surface intrinsic elliptic partial differential equations (PDEs) poses interesting challenges not seen in flat space. The discretization of these PDEs typically proceeds by either parametrizing the surface, triangulating the surface, or embedding the surface in a higher dimensional flat space. The closest point method (CPM) is an embedding method that represents surfaces using a function that maps points in the embedding space to their closest points on the surface. In the CPM, this mapping also serves as an extension operator that brings surface intrinsic data onto the embedding space, allowing PDEs to be numerically approximated by standard methods in a narrow tubular neighborhood of the surface. We focus here on numerically approximating the positive Helmholtz equation, $\left(c-\Delta_\mathcal{S}\right)u=f,~c\in\mathbb{R}^+$ by the CPM paired with finite differences. This yields a large, sparse, and non-symmetric system to solve. Herein, we develop restricted additive Schwarz (RAS) and optimized restricted additive Schwarz (ORAS) solvers and preconditioners for this discrete system. In particular, we develop a general strategy for computing overlapping partitions of the computational domain, as well as defining the corresponding Dirichlet and Robin transmission conditions. We demonstrate that the convergence of the ORAS solvers and preconditioners can be improved by using a modified transmission condition where more than two overlapping subdomains meet. Numerical experiments are provided for a variety of analytical and triangulated surfaces. We find that ORAS solvers and preconditioners outperform their RAS counterparts, and that using domain decomposition as a preconditioner gives faster convergence over using it as a solver, as expected. The methods exhibit good parallel scalability over the range of process counts tested.

math.NA↗

arXiv2015

Algorithm xxx: RIDC Methods -- A Family of Parallel Time-Integrators

Revisionist integral deferred correction (RIDC) methods are a family of parallel--in--time methods to solve systems of initial values problems. The approach is able to bootstrap lower order time integrators to provide high order approximations in approximately the same wall clock time, hence providing a multiplicative increase in the number of compute cores utilized. Here we provide a C++ framework which automatically produces a parallel--in--time solution of a system of initial value problems given user supplied code for the right hand side of the system and a sequential code for a first-order time step. The user supplied time step routine may be explicit or implicit and may make use of any auxiliary libraries which take care of the solution of any nonlinear algebraic systems which may arise or the numerical linear algebra required. The code contains six examples of increasing complexity which also serve as templates to solve user defined problems.

cs.MS↗

arXiv2012

A Computationally Stable Approach to Gaussian Process Interpolation of Deterministic Computer Simulation Data

For many expensive deterministic computer simulators, the outputs do not have replication error and the desired metamodel (or statistical emulator) is an interpolator of the observed data. Realizations of Gaussian spatial processes (GP) are commonly used to model such simulator outputs. Fitting a GP model to $n$ data points requires the computation of the inverse and determinant of $n \times n$ correlation matrices, $R$, that are sometimes computationally unstable due to near-singularity of $R$. This happens if any pair of design points are very close together in the input space. The popular approach to overcome near-singularity is to introduce a small nugget (or jitter) parameter in the model that is estimated along with other model parameters. The inclusion of a nugget in the model often causes unnecessary over-smoothing of the data. In this paper, we propose a lower bound on the nugget that minimizes the over-smoothing and an iterative regularization approach to construct a predictor that further improves the interpolation accuracy. We also show that the proposed predictor converges to the GP interpolator.

stat.ME↗