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The Poisson–Boltzmann equation (PBE) is a fundamental implicit solvent continuum model for calculating the electrostatic potential of large ionic solvated biomolecules. However, its numerical solution encounters severe challenges arising from its strong singularity and nonlinearity. In (P. Benner, V. Khoromskaia, B. Khoromskij, C. Kweyu, and M. Stein, "Regularization of Poisson-Boltzmann type equations with singular source terms using the range-separated tensor format," SIAM J. Sci. Comput., vol. 43, no. 1, pp. A415–A445, 2021; C. Kweyu, V. Khoromskaia, B. Khoromskij, M. Stein, and P. Benner, "Solution decomposition for the nonlinear Poisson-Boltzmann equation using the range-separated tensor format," arXiv:2109.14073, 2021), the effect of strong singularities was eliminated by applying the range-separated (RS) canonical tensor format (P. Benner, V. Khoromskaia, and B. N. Khoromskij, "Range-separated tensor format for many-particle modeling," SIAM J. Sci. Comput., vol. 40, no. 2, pp. A1034–A1062, 2018; B. N. Khoromskij, "Range-separated tensor representation of the discretized multidimensional Dirac delta and elliptic operator inverse," J. Comput. Phys., vol. 401, p. 108998, 2020) to construct a solution decomposition scheme for the PBE. The RS tensor format allows deriving a smooth approximation to the Dirac delta distribution in order to obtain a regularized PBE (RPBE) model. However, solving the RPBE is still computationally demanding due to its high dimension N , where N is always in the millions. In this study, we propose to apply the reduced basis method (RBM) and the (discrete) empirical interpolation method ((D)EIM) to the RPBE in order to construct a reduced order model (ROM) of low dimension N ≪ N , whose solution accurately approximates the nonlinear RPBE. The long-range potential can be obtained by lifting the ROM solution back to the N -space while the short-range potential is directly precomputed analytically, thanks to the RS tensor format. The sum of both provides the total electrostatic potential. The main computational benefit is the avoidance of computing the numerical approximation of the singular electrostatic potential. We demonstrate in the numerical experiments, the accuracy and efficacy of the reduced basis (RB) approximation to the nonlinear RPBE (NRPBE) solution and the corresponding computational savings over the classical nonlinear PBE (NPBE) as well as over the RBM being applied to the classical NPBE. [ABSTRACT FROM AUTHOR]

In this work, we study a local adaptive smoothing algorithm for a-posteriori-steered p-robust multigrid methods. The solver tackles a linear system which is generated by the discretization of a second-order elliptic diffusion problem using conforming finite elements of polynomial order p ≥ 1 {p\geq 1}. After one V-cycle ("full-smoothing" substep) of the solver of [A. Miraçi, J. Papež, and M. Vohralík, A-posteriori-steered p-robust multigrid with optimal step-sizes and adaptive number of smoothing steps, SIAM J. Sci. Comput. 2021, 10.1137/20M1349503], we dispose of a reliable, efficient, and localized estimation of the algebraic error. We use this existing result to develop our new adaptive algorithm: thanks to the information of the estimator and based on a bulk-chasing criterion, cf. [W. Dörfler, A convergent adaptive algorithm for Poisson's equation, SIAM J. Numer. Anal. 33 1996, 3, 1106–1124], we mark patches of elements with increased estimated error on all levels. Then, we proceed by a modified and cheaper V-cycle ("adaptive-smoothing" substep), which only applies smoothing in the marked regions. The proposed adaptive multigrid solver picks autonomously and adaptively the optimal step-size per level as in our previous work but also the type of smoothing per level (weighted restricted additive or additive Schwarz) and concentrates smoothing to marked regions with high error. We prove that, under a numerical condition that we verify in the algorithm, each substep (full and adaptive) contracts the error p-robustly, which is confirmed by numerical experiments. Moreover, the proposed algorithm behaves numerically robustly with respect to the number of levels as well as to the diffusion coefficient jump for a uniformly-refined hierarchy of meshes. [ABSTRACT FROM AUTHOR]

In this paper, we present a new regularization scheme for the linearized Poisson{ Boltzmann equation (PBE) which models the electrostatic potential of biomolecules in a solvent. This scheme is based on the splitting of the target potential into the short- and long-range components localized in the molecular region by using the range-separated (RS) tensor format [P. Benner, V. Khoromskaia, and B. N. Khoromskij, SIAM J. Comput., 2 (2018), pp. A1034{A1062] for representation of the discretized multiparticle Dirac delta [B. N. Khoromskij, J. Comput. Phys., 401 (2020), 108998] constituting the highly singular right-hand side in the PBE. From the computational point of view our regularization approach requires only the modification of the right-hand side in the PBE so that it can be implemented within any open-source grid-based software package for solving PBE that already includes some FEM/FDM disretization scheme for elliptic PDE and solver for the arising linear system of equations. The main computational benefits are twofold. First, one applies the chosen PBE solver only for the smooth long-range (regularized) part of the collective potential with the regular right-hand side represented by a low-rank RS tensor with a controllable precision. Thus, we eliminate the numerical treatment of the singularities in the right-hand side and do not change the interface and boundary conditions. And second, the elliptic PDE need not be solved for the singular part in the right-hand side at all, since the short-range part of the target potential of the biomolecule is precomputed independently on a computational grid by simple one-dimensional tensor operations. The total potential is then obtained by adding the numerical solution of the PBE for the smooth long-range part to the directly precomputed tensor representation for the short-range contribution. Numerical tests illustrate that the new regularization scheme, implemented by a simple modification of the right-hand side in the chosen PBE solver, improves the accuracy of the approximate solution on rather coarse grids. The scheme also demonstrates good convergence behavior on a sequence of refined grids. [ABSTRACT FROM AUTHOR]