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Hulsmann M.,Fraunhofer Institute for Algorithms and Scientific Computing | Reith D.,Bonn-Rhein-Sieg University of Applied Sciences
Entropy | Year: 2013

Molecular modeling is an important subdomain in the field of computational modeling, regarding both scientific and industrial applications. This is because computer simulations on a molecular level are a virtuous instrument to study the impact of microscopic on macroscopic phenomena. Accurate molecular models are indispensable for such simulations in order to predict physical target observables, like density, pressure, diffusion coefficients or energetic properties, quantitatively over a wide range of temperatures. Thereby, molecular interactions are described mathematically by force fields. The mathematical description includes parameters for both intramolecular and intermolecular interactions. While intramolecular force field parameters can be determined by quantum mechanics, the parameterization of the intermolecular part is often tedious. Recently, an empirical procedure, based on the minimization of a loss function between simulated and experimental physical properties, was published by the authors. Thereby, efficient gradient-based numerical optimization algorithms were used. However, empirical force field optimization is inhibited by the two following central issues appearing in molecular simulations: firstly, they are extremely time-consuming, even on modern and high-performance computer clusters, and secondly, simulation data is affected by statistical noise. The latter provokes the fact that an accurate computation of gradients or Hessians is nearly impossible close to a local or global minimum, mainly because the loss function is flat. Therefore, the question arises of whether to apply a derivative-free method approximating the loss function by an appropriate model function. In this paper, a new Sparse Grid-based Optimization Workflow (SpaGrOW) is presented, which accomplishes this task robustly and, at the same time, keeps the number of time-consuming simulations relatively small. This is achieved by an efficient sampling procedure for the approximation based on sparse grids, which is described in full detail: in order to counteract the fact that sparse grids are fully occupied on their boundaries, a mathematical transformation is applied to generate homogeneous Dirichlet boundary conditions. As the main drawback of sparse grids methods is the assumption that the function to be modeled exhibits certain smoothness properties, it has to be approximated by smooth functions first. Radial basis functions turned out to be very suitable to solve this task. The smoothing procedure and the subsequent interpolation on sparse grids are performed within sufficiently large compact trust regions of the parameter space. It is shown and explained how the combination of the three ingredients leads to a new efficient derivative-free algorithm, which has the additional advantage that it is capable of reducing the overall number of simulations by a factor of about two in comparison to gradient-based optimization methods. At the same time, the robustness with respect to statistical noise is maintained. This assertion is proven by both theoretical considerations and practical evaluations for molecular simulations on chemical example substances. © 2013 by the authors. Source

Zaretskiy Y.,Heriot - Watt University | Geiger S.,Heriot - Watt University | Sorbie K.,Heriot - Watt University | Forster M.,Fraunhofer Institute for Algorithms and Scientific Computing
Advances in Water Resources | Year: 2010

Upscaling pore-scale processes into macroscopic quantities such as hydrodynamic dispersion is still not a straightforward matter for porous media with complex pore space geometries. Recently it has become possible to obtain very realistic 3D geometries for the pore system of real rocks using either numerical reconstruction or micro-CT measurements. In this work, we present a finite element-finite volume simulation method for modeling single-phase fluid flow and solute transport in experimentally obtained 3D pore geometries. Algebraic multigrid techniques and parallelization allow us to solve the Stokes and advection-diffusion equations on large meshes with several millions of elements. We apply this method in a proof-of-concept study of a digitized Fontainebleau sandstone sample. We use the calculated velocity to simulate pore-scale solute transport and diffusion. From this, we are able to calculate the a priori emergent macroscopic hydrodynamic dispersion coefficient of the porous medium for a given molecular diffusion Dm of the solute species. By performing this calculation at a range of flow rates, we can correctly predict all of the observed flow regimes from diffusion dominated to convection dominated. © 2010 Elsevier Ltd. Source

Smith E.,Fraunhofer Institute for Algorithms and Scientific Computing
Software and Systems Modeling | Year: 2015

This article is a short summary and explanation of the scientific work of Carl Adam Petri. The very basics of net theory are sufficient to understand it. © 2014, Springer-Verlag Berlin Heidelberg. Source

Hulsmann M.,Fraunhofer Institute for Algorithms and Scientific Computing | Koddermann T.,Fraunhofer Institute for Algorithms and Scientific Computing | Vrabec J.,University of Paderborn | Reith D.,Fraunhofer Institute for Algorithms and Scientific Computing
Computer Physics Communications | Year: 2010

The concept, issues of implementation and file formats of the GRadient-based Optimization Workflow for the Automated Development of Molecular Models 'GROW' (version 1.0) software tool are described. It enables users to perform automated optimizations of force field parameters for atomistic molecular simulations by an iterative, gradient-based optimization workflow. The modularly constructed tool consists of a main control script, specific implementations and secondary control scripts for each numerical algorithm, as well as analysis scripts. Taken together, this machinery is able to automatically optimize force fields and it is extensible by developers with regard to further optimization algorithms and simulation tools. Results on nitrogen are briefly reported as a proof of concept. © 2009 Elsevier B.V. All rights reserved. Source

Hulsmann M.,Fraunhofer Institute for Algorithms and Scientific Computing | Vrabec J.,University of Paderborn | Maass A.,Fraunhofer Institute for Algorithms and Scientific Computing | Reith D.,Fraunhofer Institute for Algorithms and Scientific Computing
Computer Physics Communications | Year: 2010

In the pursuit to study the parameterization problem of molecular models with a broad perspective, this paper is focused on an isolated aspect: It is investigated, by which algorithms parameters can be best optimized simultaneously to different types of target data (experimental or theoretical) over a range of temperatures with the lowest number of iteration steps. As an example, nitrogen is regarded, where the intermolecular interactions are well described by the quadrupolar two-center Lennard-Jones model that has four state-independent parameters. The target data comprise experimental values for saturated liquid density, enthalpy of vaporization, and vapor pressure. For the purpose of testing algorithms, molecular simulations are entirely replaced by fit functions of vapor-liquid equilibrium (VLE) properties from the literature to assess efficiently the diverse numerical optimization algorithms investigated, being state-of-the-art gradient-based methods with very good convergency qualities. Additionally, artificial noise was superimposed onto the VLE fit results to evaluate the numerical optimization algorithms so that the calculation of molecular simulation data was mimicked. Large differences in the behavior of the individual optimization algorithms are found and some are identified to be capable to handle noisy function values. © 2010 Elsevier B.V. All rights reserved. Source

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