Abstract: Resistivity well logging characterizes the geological formation around a borehole by measuring the electrical resistivity. On logging while drilling techniques, real-time imaging of the well surroundings is decisive to correct the well trajectory in real time for geosteering purposes. Thus, we require numerical methods that rapidly solve Maxwell's equations. In this work, we study the main features and limitations of rIGA to solve borehole resistivity problems. We apply rIGA method to approximate 3D electromagnetic fields that result from solving Maxwell's equations through the 2.5D formulation. We use a spline-based generalization of a H(curl) x H^1 functional space. In particular, we use the H(curl) spaces previously introduced by Buffa et al. to set the high-continuity curl-conforming space discretization.

Abstract: Despite the existence of several hp-adaptive algorithms in the literature (e.g. [1]), very few are used in industrial context due to their high implementational complexity, computational cost, or both. This occurs mainly because of two limitations associated with hp-adaptive methods: (1) The data structures needed to support hp-refined meshes are often complex, and (2) the design of a robust automatic hp-adaptive strategy is challenging. To overcome limitation (1), we adopt the multi-level approach of D’Angela et al. [2]. This method handles hanging nodes via a multilevel technique with massive use of Dirichlet nodes. Our main contribution in this work is intended to overcome limitation (2) by introducing a novel automatic hp-adaptive strategy. For that, we have developed a simple energy-based coarsening approach that takes advantage of the hierarchical structure of the basis functions. Given any grid, the main idea consists in detecting those unknowns that contribute least to the energy norm, and remove them. Once a sufficient level of unrefinement is achieved, a global h, p, or any other type of refinements can be performed. We tested and analyzed our algorithm on one-dimensional (1D) and two- dimensional (2D) benchmark cases. In this presentation, we shall illustrate the main advantages and limitations of the proposed hp-adapted method. References: 1. L. Demkowicz. Computing with hp-adaptive finite elements. Vol. 1. One and two dimensional elliptic and Maxwell problems. Applied Mathematics and Nonlinear Science Series. Chapman & Hall/CRC, Boca Raton, FL, 2007. ISBN 978-1-58488- 671-6; 1-58488-671-4. 2. D. D’Angella, N. Zander, S. Kollmannsberger, F. Frischmann, E. Rank, A. Schröder, and A. Reali. Multi-level hp-adaptivity and explicit error estimation. Advanced Modeling and Simulation in Engineering Sciences, 3(1):33, 2016. ISSN 2213-7467.

Abstract: We present a framework to run Cahn-Hilliard simulations with a web interface. We use a popular Continous Integration tool Jenkins. This setup allows launching computations from any machine and without the need to sustain a connection to the computational environment. Moreover, the results of the computations are automatically post-processed and stored upon job completion for future retrieval in the form of a sequence of bitmaps, and the video illustrating the simulation. We extract the mobility and chemical potential functions from the Cahn-Hilliard equation to the interface, allowing for several numerical applications. The discretization is performed with isogeometric analysis, and it is parameterized with the number of time steps, time step size, mesh dimensions, and the order of the B-splines. The interface is linked with the fast alternating direction semi-implicit solver [1], resulting in a linear computational cost of the simulation.

Abstract: We want to estimate the effective (homogenized) compressional velocity of a highly heterogeneous porous rock at low frequencies. To achieve this goal is necessary to repeat virtually the rock domain several times until it becomes at least two-wavelengths long. Otherwise, boundary conditions (e.g., a PML) pollute the estimated effective velocity. Due to this requirement on the computational domain size, traditional conforming fitting element grids result in a humongous number of elements that cannot be simulated with today's computers. To overcome this problem, we consider non-fitting meshes, in which each finite element includes highly-discontinuous material properties. To maintain accuracy under this scenario, we show it is sufficient to perform exact integration. Being this operation also computationally expensive for such large domains, we precompute the element stiffness matrices. The presence of a PML makes the implementation of this precomputation step more challenging. In this presentation, we illustrate the main challenges for solving this upscaling/homogenization problem, which is of great interest to the oil & gas industry, and we detail the computational techniques employed to overcome them. The performance of the proposed method is also showcased with different numerical experiments.

Abstract: We present a Distributed Memory Parallel (DMP) implementation of agent based economic models, which facilitates large scale simulations with millions of agents. A major obstacle in scalable DMP implementation is balancely distributing the agents among MPI processes, while making all the topological graphs, over which the agents interact, available at a minimum communication cost. We balancely distributed the computational workload among MPI processes by partitioning a representative employer-employee interaction graph, and all the other interaction graphs are made available at negligible communication costs by mimicking the organizations of the real-world's economic entities. Cache friendly and low memory intensive algorithms and data structures are proposed to improve runtime and parallel scalability, and their effectivenesses are demonstrated. It is demonstrated that the current implementation is capable of simulating 1:1 scale models of medium size countries.

Abstract: We propose a novel computational implicit method called Isogeometric Residual Minimization (iGRM) with direction splitting. The method mixes the benefits of isogeometric analysis, implicit dynamics, residual minimization, and alternating direction solver. We utilize tensor product B-spline basis functions in space, implicit second-order time integration schemes and residual minimization at every time step. Then, we implement an implicit time integration scheme and apply, for each space-direction, a stabilized mixed method based on residual minimization. Finally, we show that the resulting system of linear equations has a Kronecker product structure, which results in a linear computational cost alternating direction solver, even using implicit time integration schemes together with the stabilized mixed formulation. We test the proposed method on three advection-diffusion computational examples, including model "membrane" problem, the circular wind problem, and the simulations modelling pollution propagating from a chimney.

Abstract: This paper proposes the use of computational methods of Complex Networks Analysis to augment the capabilities of a broker involved in multi agent freight transport negotiation.We have developed an experimentation environment that provides compelling arguments that using our proposed approach the broker is able to apply more effective negotiation strategies for gaining longer term benefits, than those offered by the standard Iterated Contract Net negotiation approach. The proposed negotiation strategies take effect on the entire population of biding agents and are driven by market inspired purposes like for example breaking monopolies and supporting agents with diverse transportation capabilities.

Abstract: We consider the problem of security-aware scheduling and load balancing in Cloud Computing systems. This optimization problem we replace by a game-theoretic approach where players tend to achieve a solution by reaching a Nash equilibrium. We propose a fully distributed algorithm based on applying iterated spatial Prisoner's Dilemma Game and a phenomenon of collective behavior of players participating in the game. Brokers representing users participate in the game to fulfill their own two criteria: the execution time of the submitted tasks and the level of provided security assurance. We experimentally show that in the process of the game a solution is found which provides an optimal resource utilization while users meet their applications’ performance and security requirements with a minimum expenditure and overhead.

Abstract: Most variational forms of isogeometric analysis use highly-continuous basis functions for both trial and test spaces. For a partial differential equation with a smooth solution, isogeometric analysis with highly-continuous basis functions for trial space results in excellent discrete approximations of the solution. However, we observe that high continuity for test spaces is not necessary. In this work, we present a framework which uses highly-continuous B-splines for the trial spaces and basis functions with minimal regularity and possibly lower order polynomials for the test spaces. To realize this goal, we adopt the residual minimization methodology. We pose the problem in a mixed formulation, which results in a system governing both the solution and a Riesz representation of the residual. We present various variational formulations which are variationally-stable and verify their equivalence numerically via numerical tests.

Abstract: Over the last decade, computational modeling has proved a useful tool to simulate and predict nosocomial transmission of pathogens and optimal control measures in healthcare settings. Nosocomial infections are a major public health issue espe-cially since the worldwide increase of antimicrobial resistance worldwide. Here, we present CTCmodeler, a framework that incorporate an agent-based model to simulate pathogen transmission through inter-individual contact in a hospital set-ting. CTCmodeler uses real admission, swab and contact data to deduce its own parameters, simulates individual-mediated transmission across hospital wards and produces weekly incidence estimates. Most earlier hospital models did not take into account the individual heterogeneity of contact patterns. By contrast, CTCmodeler explicitly captures temporal heterogeneous individual contact dy-namics by modelling close proximity interactions over time. Here, we illustrate how CTCmodeler may be used to simulate methicillin-resistant Staphylococcus aureus dissemination in a French long-term care hospital, using longitudinal data on sensor-recorded contacts and weekly swabs from the i-Bird study.

Abstract: Multi-criteria optimization problems belong to the hardest computational problems tackled, thus metaheuristic-based approach is necessary in order to deal with them. Evolutionary algorithms, swarm intelligence methods and other are very often used in such cases. Based on well-known ``no free lunch theorem'' there is always a need for creating new metaheuristics, though according to Sorensen, they should not be proposed without a firm background. In this paper a socio-cognitive ACO-type algorithm is proposed for multi-criteria TSP problem optimization. This algorithm is rooted in psychological inspirations and follows other socio-cognitive swarm intelligence methods proposed up to now. This paper presents the idea and shows the applicability of the proposed algorithm based on selected benchmark functions from the scope of well-known TSPLIB library.

Abstract: One of the key problems in parallel processing systems is the architecture of internodal connections, thus affecting the computational efficiency of the whole. In this work authors describe proposition of a new multi-channel hierarchical computational environment with distributed passive switching. According to authors, improvement of communication efficiency should be based on grouping of system components (processing nodes and channels). In the first group, processing nodes are combined into independent groups that communicate using a dedicated channel group. The second type of clustering groups channels available in the system. In particular, they are divided into smaller independent fragments that can be combined into clusters that support selected users. In this work, a model of proposed computational environment and basic reconfiguration protocol were described. The necessary components and management of reconfiguration, passive switching and hierarchization were discussed, highlighting related problems to be solved.

Abstract: This paper presents a model and implementation of a multi-agent system to support decisions to optimize a configuration of the production process in an company. Our goal is to choose the most desirable parameters of the technological process using computer simulation, which will help to avoid or reduce the number of much more expensive trial production processes, using physical production lines. These identified values of production process parameters will be applied later in the real mass production. Decision-making strategies are selected using different machine learning techniques that assist in obtaining products with the required parameters, taking into account sets of historical data. The focus was primarily on the analysis of the quality of prediction of the obtained product parameters for the different algorithms used and different sizes of historical data sets, and therefore different details of information, and secondly on the examining of the times necessary for building decision models for individual algorithms and data sets. The following algorithms were used: Multilayer Perceptron, Bagging, RandomForest, M5P and Voting. The experiments presented were carried out using data obtained for foundry processes. The JADE platform and the Weka environment were used to implement the multi--agent system.