We present a spectrally accurate method for the rapid evaluation of free-space Stokes potentials, i.e., sums involving a large number of free space Green's functions. We consider sums involving stokeslets, stresslets and rotlets that appear in boundary integral methods and potential methods for solving Stokes equations. The method combines the framework of the Spectral Ewald method for periodic problems (Lindbo and Tornberg in J Comput Phys 229(23): 8994-9010, 2010. doi:10.1016/j.jcp.2010.08. 026), with a very recent approach to solving the free-space harmonic and biharmonic equations using fast Fourier transforms (FFTs) on a uniform grid (Vico et al. in J Comput Phys 323:191-203, 2016. doi:10.1016/j.jcp.2016. Convolution with a truncated Gaussian function is used to place point sources on a grid. With precomputation of a scalar grid quantity that does not depend on these sources, the amount of oversampling of the grids with Gaussians can be kept at a factor of two, the minimum for aperiodic convolutions by FFTs. The resulting algorithm has a computational complexity of O(N log N) for problems with N sources and targets. Comparison is made with a fast multipole method to show that the performance of the new method is competitive. BackgroundIn this paper, we consider the evaluation of free-space potentials of Stokes flow, i.e., vector fields defined by sums involving a large number of free space Green's functions such as the so-called stokeslet, stresslet or rotlet. The stokeslet is the free space Green's function for velocity and is given bywith r = |r| and where δ jl is the Kronecker delta. The stresslet and rotlet will be introduced in the following. The discrete sums are on the form

A unified treatment for fast and spectrally accurate evaluation of electrostatic potentials subject to periodic boundary conditions in any or none of the three space dimensions is presented. Ewald decomposition is used to split the problem into a real space and a Fourier space part, and the FFT based Spectral Ewald (SE) method is used to accelerate the computation of the latter. A key component in the unified treatment is an FFT based solution technique for the free-space Poisson problem in three, two or one dimensions, depending on the number of non-periodic directions. The cost of calculations is furthermore reduced by employing an adaptive FFT for the doubly and singly periodic cases, allowing for different local upsampling rates. The SE method will always be most efficient for the triply periodic case as the cost for computing FFTs will be the smallest, whereas the computational cost for the rest of the algorithm is essentially independent of the periodicity. We show that the cost of removing periodic boundary conditions from one or two directions out of three will only marginally increase the total run time. Our comparisons also show that the computational cost of the SE method for the free-space case is typically about four times more expensive as compared to the triply periodic case.The Gaussian window function previously used in the SE method, is here compared to an approximation of the Kaiser-Bessel window function, recently introduced in [2]. With a carefully tuned shape parameter that is selected based on an error estimate for this new window function, runtimes for the SE method can be further reduced.

We present a fast and spectrally accurate method for efficient computation of the three dimensional Coulomb potential with periodicity in one direction. The algorithm is FFT-based and uses the so-called Ewald decomposition, which is naturally most efficient for the triply periodic case. In this paper, we show how to extend the triply periodic Spectral Ewald method to the singly periodic case, such that the cost of computing the singly periodic potential is only marginally larger than the cost of computing the potential for the corresponding triply periodic system. In the Fourier space contribution of the Ewald decomposition, a Fourier series is obtained in the periodic direction with a Fourier integral over the non periodic directions for each discrete wave number. We show that upsampling to resolve the integral is only needed for modes with small wave numbers. For the zero wave number, this Fourier integral has a singularity. For this mode, we effectively need to solve a free-space Poisson equation in two dimensions. A very recent idea by Vico et al. makes it possible to use FFTs to solve this problem, allowing us to unify the treatment of all modes. An adaptive 3D FFT can be established to apply different upsampling rates locally. The computational cost for other parts of the algorithm is essentially unchanged as compared to the triply periodic case, in total yielding only a small increase in both computational cost and memory usage for this singly periodic case.Comment: 36 pages, 19 figure

The parallel scaling of classical molecular dynamics simulations is limited by the communication of the 3D fast Fourier transform of the particle-mesh electrostatics methods, which are used by most molecular simulation packages. The Fast Multipole Method (FMM) has much lower communication requirements and would, therefore, be a promising alternative to mesh based approaches. However, the abrupt switch from direct particle-particle interactions to approximate multipole interactions causes a violation of energy conservation, which is required in molecular dynamics. To counteract this effect, higher accuracy must be requested from the FMM, leading to a substantially increased computational cost. Here, we present a regularization of the FMM that provides analytical energy conservation. This allows the use of a precision comparable to that used with particle-mesh methods, which significantly increases the efficiency. With an application to a 2D system of dipolar molecules representative of water, we show that the regularization not only provides energy conservation but also significantly improves the accuracy. The latter is possible due to the local charge neutrality in molecular systems. Additionally, we show that the regularization reduces the multipole coefficients for a 3D water model even more than in our 2D example.