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检索条件"任意字段=Conference on Simulation-Driven Modeling and Optimization"

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modeling and optimization of Complex Building Energy Systems with Deep Neural Networks

Modeling and Optimization of Complex Building Energy Systems...

引用

Asilomar conference on Signals, Systems, and Computers

作者： Yize Chen Yuanyuan Shi Baosen Zhang Department of Electrical Engineering University of Washington Seattle WA USA

Modern buildings encompass complex dynamics of multiple electrical, mechanical, and control systems. One of the biggest hurdles in applying conventional model-based optimization and control methods to building energy management is the huge cost and effort of capturing diverse and temporally correlated dynamics. Here we propose an alternative approach which is model-free and data-driven. By utilizing high volume of data coming from advanced sensors, we train a deep Recurrent Neural Networks (RNN) which could accurately represent the operation's temporal dynamics of building complexes. The trained network is then directly fitted into a constrained optimization problem with finite horizons. By reformulating the constrained optimization as an unconstrained optimization problem, we use iterative gradient descents method with momentum to find optimal control inputs. simulation results demonstrate proposed method's improved performances over model-based approach on both building system modeling and control.

关键词： Building energy management Deep learning Gradient algorithms HVAC systems

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Reduced-Cost Data-driven modeling of Antenna Structures

Reduced-Cost Data-Driven Modeling of Antenna Structures

引用

IEEE MTT-S International conference on Numerical Electromagnetic and Multiphysics modeling and optimization (NEMO)

作者： Koziel, Slawomir Cheng, Qingsha S. Reykjavik Univ Sch Sci & Engn IS-101 Reykjavik Iceland Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen 518055 Guangdong Peoples R China

ISBN: (纸本)9781467387620

Fast and accurate replacement models (surrogates) are indispensable in the design of microwave and antenna structures. Conventional data-driven modeling of antennas is usually prohibitively expensive in terms of data acquisition due to highly nonlinear responses of typical responses (e.g., reflection coefficient versus frequency). In this paper, a technique for reduced-cost modeling is proposed. The technique is based on performance-driven constraining of the surrogate model region of validity. Our approach permits dramatic reduction of the sampled portion of the design space without formally reducing geometry parameter ranges. It is demonstrated using a dielectric resonator antenna and favorably compared to conventional modeling using kriging interpolation.

关键词： Antenna design space mapping simulation-driven optimization surrogate modeling

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Cost/Performance trade-off of domain segmentation for EM-driven surrogate-assisted multi-objective antenna design

Cost/Performance trade-off of domain segmentation for EM-dri...

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Loughborough Antennas and Propagation conference, LAPC 2017

作者： Bekasiewicz, A. Koziel, S. Engineering Optimization and Modeling Center Reykjavik University Reykjavik Iceland Faculty of Electronics Telecommunications and Informatics Gdansk University of Technology Gdansk Poland

ISBN: (纸本)9781785615153

EM-driven multi-objective design of modern antenna structures is a challenging task which involves seeking for trade-offs between various electrical/field performance figures and geometrical requirements. Conventional approach for solving these problems, i.e., utilization of population-based metaheuristics, is numerically prohibitive as such methods require thousands of EM simulations to complete the optimization run. Design cost can be reduced by executing a metaheuristic algorithm on a fast data-driven model constructed from coarsely-discretized EM simulation data. One of the most recent methods exploiting such a concept, allows for low cost determination of the Pareto set by dividing the search space into a set of adjacent compartments (each of which is represented by a separate data-driven model) located along the Pareto front. In this work, the effect of setup segmentation on performance and computational cost of the design process is investigated. Our considerations are illustrated using a planar monopole antenna. A discussion on the obtained numerical results is also provided. © 2017 Institution of Engineering and Technology. All rights reserved.

关键词： Multiobjective optimization

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Two-Stage Gaussian Process modeling of Microwave Structures for Design optimization 1

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conference on simulation-driven modeling and optimization

作者： Jacobs, J. Pieter Koziel, Slawomir Univ Pretoria Dept Elect Elect & Comp Engn Ctr Electromagnetism ZA-0002 Pretoria South Africa Reykjavik Univ Sch Sci & Engn Engn Optimizat & Modeling Ctr Menntavegur 1 IS-101 Reykjavik Iceland

ISBN: (数字)9783319275178

ISBN: (纸本)9783319275178;9783319275154

Accurate models that can be rapidly evaluated are indispensable in microwave engineering. Kernel-based machine learning methods applied to the modeling of microwave structures have recently attracted attention;these include support vector regression, Bayesian support vector regression, and Gaussian process regression (GPR). In this chapter, we apply an extended methodology based on GPR, namely two-stage GPR, to the modeling of microwave antennas and filters. At the core of the method lies variable-fidelity electromagnetic simulations. In the first stage, a mapping between electromagnetic models (simulations) of low and high fidelity is learned, which allows for significantly reducing the computational effort necessary to set up the high-fidelity training data sets for the actual surrogate models (second stage), with negligible loss in predictive power. We apply the two-stage models to design optimization involving several examples of antennas and microstrip filters.

关键词： Gaussian process regression Computer-aided design (CAD) Electromagnetic (EM) simulation Microwave engineering Space mapping Surrogate-based optimization Surrogate modeling

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Geometry Scaling of UWB Antennas with Respect to Material Properties of the Substrate

Geometry Scaling of UWB Antennas with Respect to Material Pr...

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IEEE MTT-S International conference on Numerical Electromagnetic and Multiphysics modeling and optimization (NEMO)

作者： Koziel, Slawomir Bekasiewicz, Adrian Cheng, Qingsha S. Reykjavik Univ Sch Sci & Engn IS-101 Reykjavik Iceland Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen 518055 Guangdong Peoples R China

ISBN: (纸本)9781467387620

A technique for expedited redesign of UWB antennas for various substrates is presented. Our approach exploits an inverse surrogate model constructed for several reference designs optimized for selected values of the substrate permittivity. The surrogate is set up at the level of coarse-discretization EM simulation model of the antenna and, subsequently, corrected to provide prediction at the level of high-fidelity EM model level. The proposed approach is validated using a UWB monopole with the permittivity scaling range from 2.2 to 6.15.

关键词： Antenna design geometry scaling simulation-driven optimization surrogate modeling inverse modeling

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Assessment of Inverse and Direct Methods for Airfoil andWing Design 1

引用

conference on simulation-driven modeling and optimization

作者： Zhang, Mengmeng Rizzi, Arthur William Royal Inst Technol KTH S-10044 Stockholm Sweden

ISBN: (数字)9783319275178

ISBN: (纸本)9783319275178;9783319275154

The goal of aerodynamic design for airfoils and wings is to improve the performance of the lifting surfaces, e.g., by minimizing the drag. We consider here two approaches, the classical inverse design approach that finds the surface which produces desired pressure distributions, and the direct mathematical optimization based on local parameter searches, that is usually enabled by fast gradient computation, for example, by the adjoint method. The hybrid approach is to combine both of them. Each approach has its own pros and cons. In this chapter the approaches are assessed by application to the design of transonic RAE2822 airfoil and ONERA M6 wing.

关键词： Inverse design Gradient-based optimization Parametrization Drag reduction Wing surface curvature Adjoint solver

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Design optimization of LNAs and Reflectarray Antennas Using the Full-Wave simulation-Based Artificial Intelligence Models with the Novel Metaheuristic Algorithms 1

引用

conference on simulation-driven modeling and optimization

作者： Gunes, Filiz Demirel, Salih Nesil, Selahattin Yildiz Tech Univ Dept Elect & Commun Engn TR-34220 Istanbul Turkey

ISBN: (数字)9783319275178

ISBN: (纸本)9783319275178;9783319275154

In this chapter, the two primarily important highly nonlinear design problems of the contemporary microwave engineering which are "Low Noise Amplifier (LNA)"s and "Reflect-array Antenna (RA)"s are solved as "Design optimization problems." For this purpose, firstly the design problem is defined in terms of the feasible design variables (FDVs), the feasible design target space (FDTS), both of which are built up by integrating the artificial intelligence black-box models based upon the measurements or full-wave simulations and a suitable metaheuristic search algorithm. In the second stage, feasible design target (FDT) or objective function of the optimization procedure is determined as a sub-space of the FDTS. Thirdly, the cost function evaluating the objective is minimized employing a suitable metaheuristic search algorithm with respect to the FDVs. Finally the completed designs are verified by the professional Microwave Circuitor3-D EM simulators.

关键词： Microwave engineering Low-noise amplifier Reflectarray Full-wave simulation Artificial intelligence Metaheuristic search algorithm Feasible design target space Feasible design variables

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Fast Multi-Objective Aerodynamic optimization Using Space-Mapping-Corrected Multi-Fidelity Models and Kriging Interpolation 1

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conference on simulation-driven modeling and optimization

作者： Leifsson, Leifur Koziel, Slawomir Tesfahunegn, Yonatan Bekasiewicz, Adrian Iowa State Univ Dept Aerosp Engn Ames IA 50011 USA Reykjavik Univ Engn Optimizat & Modeling Ctr Sch Sci & Engn Menntavegur 1 IS-101 Reykjavik Iceland

ISBN: (数字)9783319275178

ISBN: (纸本)9783319275178;9783319275154

The chapter describes a computationally efficient procedure for multi-objective aerodynamic design optimization with multi-fidelity models, corrected using space mapping, and kriging interpolation. The optimization procedure utilizes a multi-objective evolutionary algorithm to generate an initial Pareto front which is subsequently refined iteratively using local enhancements of the kriging-based surrogate model. The refinements are realized with space mapping response correction based on a limited number of high-fidelity training points allocated along the initial Pareto front. The method yields-at a low computational cost-a set of designs representing trade-offs between the conflicting objectives. We demonstrate the approach using examples of airfoil design, one in transonic flow and another one in low-speed flow, in low-dimensional design spaces.

关键词： Multi-objective optimization Aerodynamic design Multi-fidelity CFD models Space mapping Kriging interpolation

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Agent Based simulation on Task Workflow Oriented Self-Organization of Cellular Organizational Network

Agent Based Simulation on Task Workflow Oriented Self-Organi...

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2017 2nd International conference on Computational modeling, simulation and Applied Mathematics（CMSAM 2017)

作者： Jie LIU Wei-ming ZHANG Bo LI Zhong-ju SUN Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Xi'an High-Tech Research Institute

modeling self-organization is a critical problem in computational analysis of organizational dynamics and decision support system. Task workflow oriented self-organization shows different characteristics from completely self-organization mechanism. Towards this end, a task workflow oriented self-organization model is constructed to analyze the internal mechanism and performance under different task requirement environments. The cellular organizational network is used to model the relationship structure of cellular work groups. The Agent Based simulation(ABS) is used to simulate the whole activity self-organization process and individual behaviors driven by task workflow. The model is examined through for simulation experiments. Experimental results shows the difference of the organizational performance according to different task requirements, which exhibits potential application in task workflow oriented organization design and optimization.

关键词： Task oriented self-organization Agent based simulation Cellular organizational network

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A Data-driven Large-Scale optimization Approach for Task-Specific Physics Realism in Real-Time Robotics simulation

A Data-Driven Large-Scale Optimization Approach for Task-Spe...

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IEEE International conference on simulation, modeling, and Programming for Autonomous Robots (SIMPAR)

作者： Bihlmaier, Andreas Kohlhoff, Kai Karlsruhe Inst Technol Inst Anthropomat & Robot IAR Intelligent Proc Con D-76131 Karlsruhe Germany Google Inc 1600 Amphitheatre Pkwy Mountain View CA 94043 USA

ISBN: (纸本)9781509046164

Physics-based simulation of robots requires models of the simulated robots and their environment. For a realistic simulation behavior, these models must be accurate. Their physical properties such as geometric and kinematic values, as well as dynamic parameters such as mass, inertia matrix and friction, must be modelled. Unfortunately, this problem is hard for at least two reasons. First, physics engines designed for simulation of rigid bodies in real-time cannot accurately describe many common real world phenomena, e.g. (drive) friction and grasping. Second, the prime candidate solution to the model parameter problem, classical parameter identification algorithms, although well-studied and efficient, often necessitate a significant manual engineering effort and may not be applicable due to application constraints. Thus, we present a data-driven general purpose tool, which allows to optimize model parameters for (task-specific) realistic simulation behavior. Our approach directly uses the simulator and the model under optimization to improve model parameters. The optimization process is highly distributed and uses a hybrid optimization approach based on metaheuristics and the Ceres non-linear least squares solver. The user only has to provide a configuration file that specifies which model parameter to optimize together with realism criteria and a set of reference recordings from the real robot system.

关键词： Friction

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