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Daniel Appelo

Publications and source records attributed to Daniel Appelo.

At least 19 recordsLinked to original sources
arXiv2026

Waves 2026 Book of Abstracts

WAVES 2026, 17th International Conference on Mathematical and Numerical Aspects of Wave Propagation Concordia University (John Molson Building), Montreal, Canada, June 22-26, 2026 WAVES 2026 is the seventeenth meeting in a long-running biennial series that has, throughout its history, alternated between Europe and North America to advance the mathematical and numerical study of wave propagation. Hosted at the John Molson Building of Concordia University in Montreal from June 22 to 26, 2026, the conference continues this tradition as a leading international forum where theory, computation, and application meet. The scientific program spans the full breadth of mathematical and numerical techniques for wave phenomena, from the modeling and analysis of the governing partial differential equations to the design, analysis, and implementation of efficient computational methods. Representative themes include acoustic, electromagnetic, elastic, and seismic wave propagation; scattering and inverse problems; high-frequency and asymptotic methods; finite element, boundary integral, and time-domain discretizations; and absorbing boundary conditions and domain truncation, with applications reaching across the physical sciences and engineering.

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arXiv2026

Numerical Study of Eigenvector Deflation to Accelerate the WaveHoltz Method

We present a numerical study of eigenvector deflation as a means of accelerating the WaveHoltz method for solving the Helmholtz equation. For energy-conserving (Dirichlet or Neumann) boundary conditions the WaveHoltz fixed-point iteration converges slowly at high frequency, requiring approximately $\mathcal{O}(\omega^{2d})$ iterations in $d$ dimensions. We show that deflating the eigenvectors whose eigenvalues lie nearest the driving frequency substantially reduces iteration counts, and we examine two ways of incorporating the eigenvectors: direct eigenvector deflation (DEVD), in which the forcing and iterate are projected against the deflation set, and augmented-Krylov eigenvector deflation (AUKED) using deflated conjugate gradient (DCG), augmented GMRES (AGMRES), and augmented (recycled) BICGSTAB (ABICGSTAB). The required eigenpairs can be computed efficiently with the EigenWave approach, and we demonstrate, in two dimensions, that when the number of deflation vectors grows quadratically with $\omega$ the asymptotic convergence rate remains essentially constant. Because the eigenvectors on structured grids are naturally represented as matrices, we further apply SVD-based compression to reduce their storage. Numerical experiments on single curvilinear grids discretized with summation-by-parts operators, and on overset grids illustrate the robustness and efficiency of the approach, with the deflated solver breaking even against the undeflated solver after as few as two right-hand sides, when accounting for the cost of precomputing the eigenvectors.

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arXiv2026

Gregory Nested Picard Iteration Schemes for Open Quantum Systems Governed by the Lindblad Equation

Numerical simulation of quantum computing hardware and open quantum systems governed by the Lindblad equation is challenging due to the high dimensionality of the density matrix and the need to preserve fundamental physical properties. In our previous work, we developed an arbitrary-order, low-rank, completely positive and trace preserving (CPTP) method for the Lindblad equation with time-dependent Hamiltonians by nested Picard iteration (NPI). In this work, we develop Gregory NPI schemes, which are CPTP schemes constructed by Gregory-type quadrature on equispaced nodes. The methods, which are of order up to nine, substantially reduce the computational cost compared to our previously proposed NPI schemes with Gaussian quadrature rules, while retaining high-order accuracy and structure preservation. We analyze the stability of the resulting scheme for a physics-based test equation. Numerical experiments verify the convergence of the method and demonstrate the effectiveness of the low-rank approximation. We study the performance of a previously constructed CNOT gate for both closed and open quantum systems.

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arXiv2025

Arbitrary High Order Low-rank Completely Positive and Trace Preserving (CPTP) Schemes for Lindblad Equations with Time-dependent Hamiltonian

In this paper, we develop a framework for designing arbitrary high order low-rank schemes for the Lindblad equation with time-dependent Hamiltonians. Our approach is based on nested Picard iterative integrators (NPI) and results in schemes in Kraus form that are completely positive and trace preserving (CPTP). The schemes are amenable to low rank formulations, making them suitable for problems where the matrix rank of the density matrix is small.

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arXiv2025

EigenWave: An Optimal O(N) Method for Computing Eigenvalues and Eigenvectors by Time-Filtering the Wave Equation

An algorithm named EigenWave is described to compute eigenvalues and eigenvectors of elliptic boundary value problems. The algorithm, based on the recently developed WaveHoltz scheme, solves a related time-dependent wave equation as part of an iteration. At each iteration, the solution to the wave equation is filtered in time. As the iteration progresses, the filtered solution generally contains relatively larger and larger proportions of eigenmodes whose eigenvalues are near a chosen target frequency (target eigenvalue). The ability to choose an arbitrary target frequency enables the computation of eigenvalues anywhere in the spectrum, without the need to invert an indefinite matrix, as is common with other approaches. Furthermore, the iteration can be embedded within a matrix-free Arnoldi algorithm, which enables the efficient computation of multiple eigenpairs near the target frequency. For efficiency, the time-dependent wave equation can be solved with implicit time-stepping and only about $10$ time-steps per-period are needed, independent of the mesh spacing. When the (definite) implicit time-stepping equations are solved with a multigrid algorithm, the cost of the resulting EigenWave scheme scales linearly with the number of grid points $N$ as the mesh is refined, giving an optimal $O(N)$ algorithm. The approach is demonstrated by finding eigenpairs of the Laplacian in complex geometry using overset grids. Results in two and three space dimensions are presented using second-order and fourth-order accurate approximations.

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arXiv2025

High-Order Hermite Optimization: Fast and Exact Gradient Computation in Open-Loop Quantum Optimal Control using a Discrete Adjoint Approach

This work introduces the High-Order Hermite Optimization (HOHO) method, an open-loop discrete adjoint method for quantum optimal control. Our method is the first of its kind to efficiently compute exact (discrete) gradients when using continuous, parameterized control pulses while solving the forward equations (e.g. Schrodinger's equation or the Linblad master equation) with an arbitrarily high-order Hermite Runge-Kutta method. The HOHO method is implemented in QuantumGateDesign$.$jl (https://github.com/leespen1/QuantumGateDesign.jl), an open-source software package for the Julia programming language, which we use to perform numerical experiments comparing the method to Juqbox$.$jl (https://github.com/LLNL/Juqbox.jl). For realistic model problems we observe speedups up to 775x.

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arXiv2025

An Optimal O(N) Helmholtz Solver for Complex Geometry using WaveHoltz and Overset Grids

We develop efficient and high-order accurate solvers for the Helmholtz equation on complex geometry. The schemes are based on the WaveHoltz algorithm which computes solutions of the Helmholtz equation by time-filtering solutions of the wave equation. The approach avoids the need to invert an indefinite matrix which can cause convergence difficulties for many iterative solvers for indefinite Helmholtz problems. Complex geometry is treated with overset grids which use Cartesian grids throughout most of the domain together with curvilinear grids near boundaries. The basic WaveHoltz fixed-point iteration is accelerated using GMRES and also by a deflation technique using a set of precomputed eigenmodes. The solution of the wave equation is solved efficiently with implicit time-stepping using as few as five time-steps per period, independent of the mesh size. The time-domain solver is adjusted to remove dispersion errors in time and this enables the use of such large time-steps without degrading the accuracy. When multigrid is used to solve the implicit time-stepping equations, the cost of the resulting WaveHoltz scheme scales linearly with the total number of grid points N (at fixed frequency) and is thus optimal in CPU-time and memory usage as the mesh is refined. A simple rule-of-thumb formula is provided to estimate the number of points-per-wavelength required for a p-th order accurate scheme which accounts for pollution (dispersion) errors. Numerical results are given for problems in two and three space dimensions, to second and fourth-order accuracy, and they show the potential of the approach to solve a wide range of large-scale problems.

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arXiv2025

lrAA: Low-Rank Anderson Acceleration

This paper proposes a new framework for computing low-rank solutions to nonlinear matrix equations arising from spatial discretization of nonlinear partial differential equations: low-rank Anderson acceleration (lrAA). lrAA is an adaptation of Anderson acceleration (AA), a well-known approach for solving nonlinear fixed point problems, to the low-rank format. In particular, lrAA carries out all linear and nonlinear operations in low-rank form with rank truncation using an adaptive truncation tolerance. We propose a simple scheduling strategy to update the truncation tolerance throughout the iteration according to a residual indicator. This controls the intermediate rank and iteration number effectively. To perform rank truncation for nonlinear functions, we propose a new cross approximation, which we call Cross-DEIM, with adaptive error control that is based on the discrete empirical interpolation method (DEIM). Cross-DEIM employs an iterative update between the approximate singular value decomposition (SVD) and cross approximation. It naturally incorporates a warm-start strategy for each lrAA iterate. We demonstrate the superior performance of lrAA applied to a range of linear and nonlinear problems, including those arising from finite difference discretizations of Laplace's equation, the Bratu problem, the elliptic Monge-Amp\'ere equation and the Allen-Cahn equation.

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arXiv2024

Preconditioning Low Rank Generalized Minimal Residual Method (GMRES) for Implicit Discretizations of Matrix Differential Equations

This work proposes a new class of preconditioners for the low rank Generalized Minimal Residual Method (GMRES) for multiterm matrix equations arising from implicit timestepping of linear matrix differential equations. We are interested in computing low rank solutions to matrix equations, e.g. arising from spatial discretization of stiff partial differential equations (PDEs). The low rank GMRES method is a particular class of Krylov subspace method where the iteration is performed on the low rank factors of the solution. Such methods can exploit the low rank property of the solution to save on computational and storage cost. Of critical importance for the efficiency and applicability of the low rank GMRES method is the availability of an effective low rank preconditioner that operates directly on the low rank factors of the solution and that can limit the iteration count and the maximal Krylov rank. The preconditioner we propose here is based on the basis update and Galerkin (BUG) method, resulting from the dynamic low rank approximation. It is a nonlinear preconditioner for the low rank GMRES scheme that naturally operates on the low rank factors. Extensive numerical tests show that this new preconditioner is highly efficient in limiting iteration count and maximal Krylov rank. We show that the preconditioner performs well for general diffusion equations including highly challenging problems, e.g. high contrast, anisotropic equations. Further, it compares favorably with the state of the art exponential sum preconditioner. We also propose a hybrid BUG - exponential sum preconditioner based on alternating between the two preconditioners.

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arXiv2024

Convergence of the Semi-Discrete WaveHoltz Iteration

In this paper we prove that for stable semi-discretizations of the wave equation for the WaveHoltz iteration is guaranteed to converge to an approximate solution of the corresponding frequency domain problem, if it exists. We show that for certain classes of frequency domain problems, the WaveHoltz iteration without acceleration converges in $O({\omega})$ iterations with the constant factor depending logarithmically on the desired tolerance. We conjecture that the Helmholtz problem in open domains with no trapping waves is one such class of problems and we provide numerical examples in one and two dimensions using finite differences and discontinuous Galerkin discretizations which demonstrate these converge results.

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arXiv2024

Energy-Conserving Hermite Methods for Maxwell's Equations

Energy-conserving Hermite methods for solving Maxwell's equations in dielectric and dispersive media are described and analyzed. In three space dimensions methods of order $2m$ to $2m+2$ require $(m+1)^3$ degrees-of-freedom per node for each field variable and can be explicitly marched in time with steps independent of $m$. We prove stability for time steps limited only by domain-of-dependence requirements along with error estimates in a special seminorm associated with the interpolation process. Numerical experiments are presented which demonstrate that Hermite methods of very high order enable the efficient simulation of electromagnetic wave propagation over thousands of wavelengths.

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arXiv2023

Mathematical approaches for characterization, control, calibration and validation of a quantum computing device

Quantum computing has received significant amounts of interest from many different research communities over the last few years. Although there are many introductory texts that focus on the algorithmic parts of quantum computing, there is a dearth of publications that describe the modeling, calibration and operation of current quantum computing devices. One aim of this report is to fill that void by providing a case study that walks through the entire procedure from the characterization and optimal control of a qudit device at Lawrence Livermore National Laboratory (LLNL) to the validation of the results. A goal of the report is to provide an introduction for students and researchers, especially computational mathematicians, who are interested in but new to quantum computing. Both experimental and mathematical aspects of this procedure are discussed. We present a description of the LLNL QuDIT testbed, the mathematical models that are used to describe it, and the numerical methods that are used to to design optimal controls. We also present experimental and computational methods that can be used to characterize a quantum device. Finally, an experimental validation of an optimized control pulse is presented, which relies on the accuracy of the characterization and the optimal control methodologies.

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arXiv2021

Fourier Continuation Discontinuous Galerkin Methods for Linear Hyperbolic Problems

Fourier continuation is an approach used to create periodic extensions of non-periodic functions in order to obtain highly-accurate Fourier expansions. These methods have been used in PDE-solvers and have demonstrated high-order convergence and spectrally accurate dispersion relations in numerical experiments. Discontinuous Galerkin (DG) methods are increasingly used for solving PDEs and, as all Galerkin formulations, come with a strong framework for proving stability and convergence. Here we propose the use of Fourier continuation in forming a new basis for the DG framework.

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arXiv2020

An energy-based discontinuous Galerkin method for semilinear wave equations

We generalize the energy-based discontinuous Galerkin method proposed in [SIAM J. Num. Anal., 53(6):2705-2726, 2015.] to second-order semilinear wave equations. A stability and convergence analysis is presented along with numerical experiments demonstrating optimal convergence for certain choices of the interelement fluxes. Applications to the sine-Gordon equation include simulations of breathers, kink, and anti-kink solitons.

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arXiv2019

WaveHoltz: Iterative Solution of the Helmholtz Equation via the Wave Equation

A new idea for iterative solution of the Helmholtz equation is presented. We show that the iteration which we denote WaveHoltz and which filters the solution to the wave equation with harmonic data evolved over one period, corresponds to a coercive operator or a positive definite matrix in the discretized case.

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arXiv2019

An energy-based discontinuous Galerkin method for the wave equation with advection

An energy-based discontinuous Galerkin method for the advective wave equation is proposed and analyzed. Energy-conserving or energy-dissipating methods follow from simple, mesh-independent choices of the inter-element fluxes, and both subsonic and supersonic advection is allowed. Error estimates in the energy norm are established, and numerical experiments on structured grids display optimal convergence in the $L^2$ norm for upwind fluxes. The method generalizes earlier work on energy-based discontinuous Galerkin methods for second order wave equations which was restricted to energy forms written as a simple sum of kinetic and potential energy.

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arXiv2018

HPS Accelerated Spectral Solvers for Time Dependent Problems

A high-order convergent numerical method for solving linear and non-linear parabolic PDEs is presented. The time-stepping is done via an explicit, singly diagonally implicit Runge-Kutta (ESDIRK) method of order 4 or 5, and for the implicit solve, we use the recently developed "Hierarchial Poincare-Steklov (HPS)" method. The HPS method combines a multidomain spectral collocation discretization technique (a "patching method") with a nested-dissection type direct solver. In the context under consideration, the elliptic solve required in each time-step involves the same coefficient matrix, which makes the use of a direct solver particularly effective. The manuscript describes the methodology and presents numerical experiments.

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