Titlebook: Computational Science – ICCS 2020; 20th International C Valeria V. Krzhizhanovskaya,Gábor Závodszky,João T Conference proceedings 2020 Spri

洞察力

Scalable Signal-Based Simulation of Autonomous Beings in Complex Environmentsvironments, numerous populations of beings, and to increase the detail of models causes the need for parallelization of computations. The signal-based simulation algorithm, presented in our previous research, prove the possibility of linear scalability of such computations up to thousands of computi

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Design of Loss Functions for Solving Inverse Problems Using Deep Learningry operations, and/or energy production. There exist different methods for solving inverse problems, including gradient based methods, statistics based methods, and Deep Learning (DL) methods. In this work, we focus on the latest. Specifically, we study the design of proper loss functions for dealin

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Allege

MeshingNet: A New Mesh Generation Method Based on Deep Learningsly unseen problem. The framework that we have developed is based around training an artificial neural network (ANN) to guide standard mesh generation software, based upon a prediction of the required local mesh density throughout the domain. We describe the training regime that is proposed, based u

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A Block Preconditioner for Scalable Large Scale Finite Element Incompressible Flow Simulations simulations performed by the stabilized finite element method. We select a set of adjustable parameters for the preconditioner and show how to tune the parameters in order to obtain fast convergence of the standard GMRES solver in which the preconditioner is employed. Additionally, we show some det

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ILEUM

Computational Complexity of Hierarchically Adapted Meshes in time complexity not worse than ., where . is the number of nodes and . is the dimensionality of the singularity. In particular, we show that this formula does not change depending on the spatial dimensionality of the mesh. We also show the relationship between the time complexity and the Kolmogo

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Computational Science – ICCS 2020978-3-030-50420-5Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Palgrave Studies in Global Higher Educatione parts of adaptive dynamically changing tree. The paper focuses on adaptation of the classic HGS algorithm for multi-criteria optimization problems, coupling the HGS with Particle Swarm Optimization demes. The main contribution of the paper is showing the efficacy and efficiency of the actor-based implementation of this metaheuristic algorithm.