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

共 1171 条记录，以下是461-470 订阅

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simulation of nozzle position optimization based on single-phase flow

Simulation of nozzle position optimization based on single-p...

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6th International conference on Mechanical Engineering, Materials Science and Civil Engineering (ICMEMSCE)

作者： Chen, Guang Zhou, Yuancong Qian, Xuelin Wu, Wenmin Zhao, Yifan Hu, Yumei Chongqing Univ State Key Lab Mech Transmiss Chongqing 400044 Peoples R China Chinese Helicopter Desigh & Res Inst Jingdezhen City Jiangxi Peoples R China

The numerical simulation on the lubrication of the complex gearbox was studied and an optimization for its injection lubrication system was carried out. Firstly, a pair of high speed gear test bench was built and related experiments were completed. The experimental results verified the correctness of the numerical simulation modeling method. Then the numerical model of the injection lubrication of the complex gear box was established, the simulation analysis was carried out. The results show that the overlong injection distance, the angle of the end face and the arrangement of the nozzle in the out of mesh side are not good for lubrication. And there is a trace in the center plane of the two meshing gear, along which the ejected oil will be under the least impact of the high speed airflow that driven by the rotating gears. When the nozzle axis is in the direction of the trace, the gear lubrication condition is the best.

关键词： Nozzles

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Validating optimization with uncertain constraints 19

Validating optimization with uncertain constraints

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Proceedings of the Winter simulation conference

作者： Henry Lam Huajie Qian Columbia University

ISBN: (纸本)9781728132839

We consider optimization with uncertain or probabilistic constraints under the availability of limited data or Monte Carlo samples. In this situation, the obtained solutions are subject to statistical noises that affect both the feasibility and the objective performance. To guarantee feasibility, common approaches in data-driven optimization impose constraint reformulations that are "safe" enough to ensure solution feasibility with high confidence. Often times, selecting this safety margin relies on loose statistical estimates, in turn leading to overly conservative and suboptimal solutions. We propose a validation-based framework to balance the feasibility-optimality tradeoff more efficiently, by leveraging the typical low-dimensional structure of solution paths in these data-driven reformulations instead of estimates based on the whole decision space utilized by past approaches. We demonstrate how our approach can lead to a feasible solution with less conservative safety adjustment and confidence guarantees.

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Data-driven optimal transport cost selection for distributionally robust optimization 19

Data-driven optimal transport cost selection for distributio...

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Proceedings of the Winter simulation conference

作者： Jose Blanchet Yang Kang Karthyek Murthy Fan Zhang Stanford University Columbia University Singapore University of Technology & Design

ISBN: (纸本)9781728132839

Some recent works showed that several machine learning algorithms, such as square-root Lasso, Support Vector Machines, and regularized logistic regression, among many others, can be represented exactly as distributionally robust optimization (DRO) problems. The distributional uncertainty set is defined as a neighborhood centered at the empirical distribution, and the neighborhood is measured by optimal transport distance. In this paper, we propose a methodology which learns such neighborhood in a natural data-driven way. We show rigorously that our framework encompasses adaptive regularization as a particular case. Moreover, we demonstrate empirically that our proposed methodology is able to improve upon a wide range of popular machine learning estimators.

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Subspace communication driven search for high dimensional optimization 19

Subspace communication driven search for high dimensional op...

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Proceedings of the Winter simulation conference

作者： Logan Mathesen Kaushik Keezhnagar Chandrasekar Xinsheng Li Giulia Pedrielli K. Selçuk Candan Arizona State University

ISBN: (纸本)9781728132839

Global optimization techniques often suffer the curse of dimensionality. In an attempt to face this challenge, high dimensional search techniques try to identify and leverage upon the effective, lower, dimensionality of the problem either in the original or in a transformed space. As a result, algorithms search for and exploit a projection or create a random embedding. Our approach avoids modeling of high dimensional spaces, and the assumption of low effective dimensionality. We argue that effectively high dimensional functions can be recursively optimized over sets of complementary lower dimensional subspaces. In this light, we propose the novel Subspace Communication for optimization (SCOOP) algorithm, which enables intelligent information sharing among subspaces such that each subspace guides the other towards improved locations. The experiments show that the accuracy of SCOOP rivals the state-of-the-art global optimization techniques, while being several orders of magnitude faster and having better scalability against the problem dimensionality.

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Developing a Flexible simulation-optimization Framework to Facilitate Sustainable Urban Drainage Systems Designs Through Software Reuse 18th

Developing a Flexible Simulation-Optimization Framework to F...

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18th International conference on Software and Systems Reuse (ICSR)

作者： Yang, Yang Chui, Ting Fong May Univ Hong Kong Dept Civil Engn Pokfulam Hong Kong Peoples R China

ISBN: (纸本)9783030228880;9783030228873

In stormwater management, increasing attention is paid to sustainable urban drainage systems (SuDS), which include porous pavements and rain gardens. Various numerical modeling software systems have been developed to simulate the hydrological performances of SuDS, e.g., GIFMOD and SWMM can be respectively applied to study the hydrological processes in small-scale SuDS and their effectiveness in large-scale drainage networks. However, it is sometimes desirable to combine the features of different software, such that the hydrological processes of various spatial-temporal scales can be simulated more accurately. Reusing the existing code to create new software, however, can be challenging, as various combinations of the software of very different structures might be interested. Therefore, this study develops a method and a toolbox in R programming language to couple and reuse existing software without modifying their source code. This toolbox automates the software application processes, including input file creation, simulation, and post-processing. A modeling scheme is adopted that the outflow time series from SuDS are first modeled using the software of choice, which are then treated as external inflows to the drainage networks modeling software. This toolbox also integrates evolutionary optimization algorithms and performance-assessment frameworks to form a flexible simulation-optimization framework for solving design optimization problems. For demonstration, SWMM, a data-driven model of SuDS, and a relative performance evaluation framework are coupled to solve a SuDS allocation problem in an urban catchment. This research demonstrates that file-based software coupling methods can be useful for reusing existing modeling software in developing integrated modeling systems.

关键词： Urban hydrology Numerical modeling Software reuse

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DEM data-driven modeling of repose angle of granular materials

DEM data-driven modeling of repose angle of granular materia...

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International conference on Industrial Artificial Intelligence (IAI)

作者： Zhou Hu Xiaoyan Liu Cancan Chu College of Electrical and Information Engineering Hunan University Changsha China

ISBN: (数字)9781728182162

ISBN: (纸本)9781728182179

Repose angle is an important property of granular materials and is usually simulated using Discrete Element Method (DEM). However, DEM simulation is computationally intensive and is thus unsuitable for online applications where parameters are frequently changed. To solve this problem, we propose a DEM data-driven modeling method for fast prediction of repose angle. Firstly, variables affecting the repose angle are analyzed; by Latin hypercube sampling of parameter spaces, 100 sets of DEM simulations are performed to generate data of repose angle. Based on these data, a support vector machine (SVM) model is then established and trained for fast prediction of repose angle under various conditions. Tests and comparison show that the reposed angle predicted by the SVM model is close to the DEM simulation result while the required computing time is greatly decreased (from 43.8 hours to 0.17 seconds), and it outperforms BP neural network and Kriging interpolation method in terms of prediction accuracy. The SVM model for repose angle is also verified by physical experiments, with prediction error less than ± 1 °. The established model can replace DEM, and is suitable for applications where fast prediction of repose angle is required.

关键词： Support vector machines Predictive models Computational modeling Friction Data-driven modeling optimization Kernel

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System and Application Performance modeling and simulation in the AI Era 20

System and Application Performance Modeling and Simulation i...

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Proceedings of the 2020 ACM SIGSIM conference on Principles of Advanced Discrete simulation

作者： Adolfy Hoisie Brookhaven National Laboratory Upton NY USA

ISBN: (纸本)9781450375924

The increasing complexity and heterogeneity of systems at large scale, combined with challenging characteristics of applications driven by data dominated by adaptivity and irregularity, pose a need for fundamental rethinking and retooling of modeling and simulation (ModSim) for systems and applications. ModSim as a science and practice will be discussed from the perspectives of methods and tools and its myriad of uses, such as system-application co-design, performance prediction, or system and application optimization. To achieve this demanding goal, the presentation initially will offer an analysis and critique of the traditional methodologies and their state of the art. Then, attention will focus on new ideas related to machine learning-both as an increasingly important application workload and a method for ModSim. The context of mapping these applications to leading-edge systems will include analysis and the need for "dynamic performance modeling" as an actionable way to optimize effectively for performance during execution. Throughout, particular emphasis will be on methods and practices that are practical, accurate, and can be applied to extreme-scale computing (as broadly defined).

关键词： modeling simulation modsim artificial intelligence systems

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Towards Data-driven simulation of End-to-end Network Performance Indicators 90

Towards Data-driven Simulation of End-to-end Network Perform...

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90th IEEE Vehicular Technology conference (IEEE VTC-Fall)

作者： Sliwa, Benjamin Wietfeld, Christian TU Dortmund Univ Commun Networks Inst D-44227 Dortmund Germany

ISBN: (纸本)9781728112206

Novel vehicular communication methods are mostly analyzed simulatively or analytically as real world performance tests are highly time-consuming and cost-intense. Moreover, the high number of uncontrollable effects makes it practically impossible to reevaluate different approaches under the exact same conditions. However, as these methods massively simplify the effects of the radio environment and various cross-layer interdependencies, the results of end-to-end indicators (e.g., the resulting data rate) often differ significantly from real world measurements. In this paper, we present a data-driven approach that exploits a combination of multiple machine learning methods for modeling the end-to-end behavior of network performance indicators within vehicular networks. The proposed approach can be exploited for fast and close to reality evaluation and optimization of new methods in a controllable environment as it implicitly considers cross-layer dependencies between measurable features. Within an example case study for opportunistic vehicular data transfer, the proposed approach is validated against real world measurements and a classical system-level network simulation setup. Although the proposed method does only require a fraction of the computation time of the latter, it achieves a significantly better match with the real world evaluations.

关键词： Predictive models Data models Ground penetrating radar Analytical models Machine learning Computational modeling Task analysis

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Exploratory Analysis to Address Deep Uncertainty – Using Calibratable System Models for Exploratory simulation of Complex Missions

Exploratory Analysis to Address Deep Uncertainty – Using Ca...

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Spring simulation conference (SpringSim)

作者： Andreas Tolk Khuong Dinh Kevin Comer Steve Scott The MITRE Corporation Hampton VA United States The MITRE Corporation McLean VA United States

ISBN: (数字)9781565553705

ISBN: (纸本)9781728163642

Traditional optimization methods and sometimes even the underlaying concept of experimentation design for optimization problems are not always successful in complex solution spaces. This is not only driven by the complexity itself, but also by the deep uncertainty in estimating the model parameters. Applying exploratory analysis to better understand the solution space has been recognized as a promising approach for several years, but with the new generation of computers the community now has the means to practically apply it as well. We define the terms, derive a conceptual model, provide an application case from the defense domain, and show the possibility to generalize the results, namely that parameter sweeps are possible and lead to deeper insights of the solution space, allowing for stable and robust solutions that often are more important in practice than optimized point solutions.

关键词： Complexity theory Uncertainty Analytical models Computational modeling Data models Robustness

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Metamodels and Iterative Design Correction for Rapid optimization of Compact Microwave Components

Metamodels and Iterative Design Correction for Rapid Optimiz...

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International conference on Numerical Electromagnetic modeling and optimization for RF, Microwave, and Terahertz Applications (NEMO)

作者： Anna Pietrenko-Dabrowska Slawomir Koziel Qingsha S. Cheng Zhen Zhang Faculty of Electr. Telecom. Inf. Gdansk Univ. of Technology Gdansk Poland Engineering Optimization & Modeling Center Reykjavik University Reykjavik Iceland Southern University of Science and Tech. Shenzhen China Harbin Institute of Technology Harbin Heilongjiang China

ISBN: (数字)9781728169668

ISBN: (纸本)9781728169675

Electromagnetic (EM)-driven design has become a commonplace of contemporary microwave engineering, primary reasons being reliability and versatility of EM analysis. For certain classes of structures such as miniaturized components, it is a practical necessity as simpler representations (e.g., equivalent networks) are either inadequate or simply not available. On the other hand, simulation-based procedures such as parameter tuning or statistical analysis entail considerable computational expenses. This paper proposes a technique for accelerated design optimization of compact microwave structures. Our methodology involves kriging metamodels constructed using already available designs as well as an iterative correction procedure, feeding the accumulated discrepancies between the target and the actual design objective values back to the metamodel to yield improved predictions. The presented approach is comprehensively validated using a three-section impedance matching transformer and compared to a gradient-based correction scheme. As demonstrated, the designs optimized for wide ranges of the operating bandwidths are obtained at the cost of just a few EM analyzes of the transformer circuit.

关键词： Microwave theory and techniques Microwave circuits Numerical models Integrated circuit modeling Electromagnetics Tuning optimization

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