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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361-370 订阅

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Data-driven optimization of Lead-Free Perovskite Solar Cell Architecture Using Machine Learning and SCAPS simulations

Data-Driven Optimization of Lead-Free Perovskite Solar Cell ...

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International conference on Electrical and Computer Engineering (ICECE)

作者： Md. Arifur Rahman Mohammad Jahangir Alam Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (数字)9798331531782

ISBN: (纸本)9798331507923

Significant advancements have been made in the study, research, and optimization of electrical devices and materials using machine learning (ML) models. Data regression and prediction provide for accurate estimates of device performance across a variety of factors by utilizing machine learning algorithms and input information. This study utilizes an ML algorithm to analyze the impact of 9 parameters on the output characteristics of methylammonium tin halide perovskite (CH 3 NH 3 SnX 3 ) solar cells. The perovskite device structure proposed here is FTO/ETL/CH3NH3SnX3/HTL/Au. Distinct parameters like doping levels, defect densities, and thickness of the diverse materials are analyzed to understand their impacts on device performance. A dataset of 2565 points was created using validated numerical simulations for training and testing the model. The PCE variation across the two metal halides was investigated, yielding a maximum PCE of 24.31% for CH 3 NH 3 SnBr 3 and $27.37 \%$ for CH 3 NH 3 SnI 3 . Using the SCAPS-1D simulation, the effects of several HTLs are examined. The device’s PCE rose from $27.37 \%$ to $39.68 \%$ by adjusting intrinsic material factors like absorber layer thickness, HTL thickness, doping, and defect concentration. A trained ML model further predicts PSC performance metrics with 91% accuracy. Consequently, the Al/FTO/PCBM/CH3NH3SnI3/V2O5/Au configuration is identified as the most suitable for manufacturing highly efficient photovoltaic devices.

关键词： Training Accuracy Machine learning algorithms Photovoltaic cells Machine learning Doping Tin Perovskites Semiconductor process modeling optimization

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AutoML for neuromorphic computing and application-driven co-design: asynchronous, massively parallel optimization of spiking architectures

AutoML for neuromorphic computing and application-driven co-...

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IEEE International conference on Rebooting Computing (ICRC)

作者： Angel Yanguas-Gil Sandeep Madireddy Applied Materials Division Argonne National Laboratory Lemont IL USA Mathematics and Computer Science Division Argonne National Laboratory Lemont IL USA

In this work we have extended AutoML inspired approaches to the exploration and optimization of neuromorphic architectures. Through the integration of a parallel asynchronous model-based search approach with a simulation framework to simulate spiking architectures, we are able to efficiently explore the configuration space of neuromorphic architectures and identify the subset of conditions leading to the highest performance in a targeted application. We have demonstrated this approach on an exemplar case of real time, on-chip learning application. Our results indicate that we can effectively use optimization approaches to optimize complex architectures, therefore providing a viable pathway towards application-driven codesign.

关键词： Neuromorphics Computational modeling Supervised learning Stochastic processes Computer architecture Signal processing Space exploration

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DrivAerNet++: a large-scale multimodal car dataset with computational fluid dynamics simulations and deep learning benchmarks 24

DrivAerNet++: a large-scale multimodal car dataset with comp...

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Proceedings of the 38th International conference on Neural Information Processing Systems

作者： Mohamed Elrefaie Florin Morar Angela Dai Faez Ahmed Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge MA Morphing and Optimization Solutions BETA CAE SYSTEMS USA Inc Farmington Hills MI Department of Computer Science Technical University of Munich Garching Germany

ISBN: (纸本)9798331314385

We present DrivAerNet++, the largest and most comprehensive multimodal dataset for aerodynamic car design. DrivAerNet++ comprises 8,000 diverse car designs modeled with high-fidelity computational fluid dynamics (CFD) simulations. The dataset includes diverse car configurations such as fastback, notchback, and estateback, with different underbody and wheel designs to represent both internal combustion engines and electric vehicles. Each entry in the dataset features detailed 3D meshes, parametric models, aerodynamic coefficients, and extensive flow and surface field data, along with segmented parts for car classification and point cloud data. This dataset supports a wide array of machine learning applications including data-driven design optimization, generative modeling, surrogate model training, CFD simulation acceleration, and geometric classification. With more than 39 TB of publicly available engineering data, DrivAerNet++ fills a significant gap in available resources, providing high-quality, diverse data to enhance model training, promote generalization, and accelerate automotive design processes. Along with rigorous dataset validation, we also provide ML benchmarking results on the task of aerodynamic drag prediction, showcasing the breadth of applications supported by our dataset. This dataset is set to significantly impact automotive design and broader engineering disciplines by fostering innovation and improving the fidelity of aerodynamic evaluations. Dataset and code available at: https://***/Mohamedelrefaie/DrivAerNet

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Event-Triggered Prediction of Blast Furnace Gas Generation Based on a Trend Self-Adaption Scheme

Event-Triggered Prediction of Blast Furnace Gas Generation B...

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Asian Control conference

作者： Guanglin Cong Long Chen Jun Zhao Feng Jin Wei Wang Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

The prediction of blast furnace gas (BFG) generation in ironmaking processes is crucial for the BFG scheduling work. Due to frequent switching of working conditions of the ironmaking process, the generation of BFG under each working condition fluctuates greatly. It is difficult to predict accurately during the transition of working conditions. In order to address these problems, this paper proposes a data-driven BFG generation prediction method based on a combination of an event-triggered scheme and a trend self-adaption scheme. In this method, different working conditions are divided by events such as the change of supply of hot air and oxygen. Considering the process characteristics of air reduction and restoration, the decrease and increase trends are predicted by a parameter adaption-based linear logarithmic regression. The model parameters are corrected online to realize trend self-discrimination. Besides, as for the residual sequence after trend fitting, it is modeled by using the long short-term memory (LSTM) network. To validate the effectiveness of the proposed method, actual industrial field data of a steel plant in China are utilized. simulation experimental results show that the proposed method significantly improves prediction accuracy of the BFG generation flow.

关键词： Employee welfare Adaptation models Atmospheric modeling Time series analysis Switches Predictive models Market research

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simulation optimization BASED FEATURE SELECTION, A STUDY ON DATA-driven optimization WITH INPUT UNCERTAINTY

SIMULATION OPTIMIZATION BASED FEATURE SELECTION, A STUDY ON ...

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Winter simulation conference

作者： Vahdat, Kimia Shashaani, Sara North Carolina State Univ Edward P Fitts Dept Ind & Syst Engn 915 Partners Way Raleigh NC 27606 USA

ISBN: (纸本)9781728194998

In machine learning, removing uninformative or redundant features from a dataset can significantly improve the construction, analysis, and interpretation of the prediction models, especially when the set of collected features is extensive. We approach this challenge with simulation optimization over a high dimensional binary space in place of the classic greedy search in forward or backward selection or regularization methods. We use genetic algorithms to generate scenarios, bootstrapping to estimate the contribution of the intrinsic and extrinsic noise and sampling strategies to expedite the procedure. By including the uncertainty from the input data in the measurement of the estimators' variability, the new framework obtains robustness and efficiency. Our results on a simulated dataset exhibit improvement over state-of-the-art accuracy, interpretability, and reliability. Our proposed framework provides insight for leveraging Monte Carlo methodology in probabilistic data-driven modeling and analysis.

关键词： Genetic algorithms

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GlorEST: A Demand Power Forecasting Automated Machine Learning Tree-based Pipeline optimization Predictive Precise Model

GlorEST: A Demand Power Forecasting Automated Machine Learni...

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Technology and Energy Management (ICTEM), International conference on

作者： Ashkan Safari Amir A. Ghavifekr Mahmood Seyyedzadeh Alireza Tajdid Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran

ISBN: (数字)9798350329797

ISBN: (纸本)9798350329803

Predictive AI-driven models play a considerable role in anticipating and managing electricity demand within smart power systems. By employing sophisticated algorithms to analyze historical consumption patterns, real-time data, and external factors, these models facilitate accurate forecasting crucial for optimizing energy distribution and enhancing grid reliability. This paper introduces GlorEST, an advanced modeling framework specifically tailored for the German electricity market. Utilizing a comprehensive dataset, GlorEST incorporates key features such as conventional and solar power supply, demand peaks, renewable energy contributions, trade dynamics, storage, and efficiency metrics. The simulation section dynamically captures the intricate interplay of these factors, offering a holistic view of the market dynamics. GlorEST’s accuracy is validated through key performance indicators (KPIs) with a low Mean Absolute Error (MAE) of 0.0489, a high R-squared (R2) of 0.9941, and precise predictions reflected in the low Mean Absolute Percentage Error (MAPE) of 0.8691. Comparative analysis against LightGBM, XGBoost, and KNN underscores GlorEST’s superior performance, positioning it as a reliable and versatile tool for decision-making and scenario analysis in the dynamic landscape of the German electricity market.

关键词： Accuracy Power system dynamics Pipelines Key performance indicator Predictive models Electricity supply industry Prediction algorithms

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Generating Synthetic Populations Based on German Census Data

Generating Synthetic Populations Based on German Census Data

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2021 Winter simulation conference, WSC 2021

作者： Ponge, Johannes Enbergs, Malte Schungel, Michael Hellingrath, Bernd Karch, Andre Ludwig, Stephan University of Münster Department of Information Systems Münster48149 Germany Institute Epidemiology Social Medicine University of Münster Münster48149 Germany Institute of Virology University of Münster Münster48149 Germany

ISBN: (纸本)9781665433112

Spatial agent-based simulations of infectious disease epidemics require a high-resolution regional population model. However, only aggregated demographic data is available for most geographic regions. Furthermore, the infectious disease application case can require the fusion of multiple data sources (e.g. census and public health statistics), inducing demand for a modular and extensible modeling approach. In this work we provide a novel sequential sample-free approach to generate synthetic baseline populations for agent-based simulations, combining synthetic reconstruction and combinatorial optimization. We applied the approach to generate a population model for the German state of North Rhine-Westphalia (17.5 million inhabitants) which yielded an average accuracy of around 98% per attribute. The resulting population model is publicly available and has been utilized in multiple simulation-based infectious disease case studies. We suggest that our research can pave the way for more geographically granular synthetic populations to be used in model-driven infectious disease epidemics prediction and prevention. © 2021 IEEE.

关键词： Combinatorial optimization

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A simulation driven optimization algorithm for scheduling sorting center operations 21

A simulation driven optimization algorithm for scheduling so...

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Proceedings of the Winter simulation conference

作者： Supratim Ghosh Aritra Pal Prashant Kumar Ankush Ojha Aditya A. Paranjape Souvik Barat Harshad Khadilkar Tata Consultancy Services Research Noida UP India

Parcel sorting operations in logistics enterprises aim to achieve a high throughput of parcels through sorting centers. These sorting centers are composed of large circular conveyor belts on which incoming parcels are placed, with multiple arms known as chutes for sorting the parcels by destination, followed by packing into roller cages and loading onto outbound trucks. Modern sorting systems need to complement their hardware innovations with sophisticated algorithms and software to map destinations and workforce to specific chutes. While state of the art systems operate with fixed mappings, we propose an optimization approach that runs before every shift, and uses real-time forecast of destination demand and labor availability in order to maximize throughput. We use simulation to improve the performance and robustness of the optimization solution to stochasticity in the environment, through closed-loop tuning of the optimization parameters.

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Tutorial: Large-Scale Spiking Neuromorphic Architecture Exploration using SANA-FE

Tutorial: Large-Scale Spiking Neuromorphic Architecture Expl...

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International conference on Hardware/Software Codesign and System Synthesis (CODES)

作者： James A. Boyle Mark Plagge Suma G. Cardwell Frances S. Chanc Andreas Gerstlauer The University of Texas at Austin Texas USA Sandia National Laboratories New Mexico USA

ISBN: (数字)9798350356397

ISBN: (纸本)9798350356403

Neuromorphic computing uses brain-inspired concepts to accelerate and efficiently execute a wide range of applications, such as mimicking biological circuits, solving NPhard optimization problems and accelerating machine learning at the edge. In particular, neuromorphic architectures to efficiently execute Spiking Neural Networks (SNNs) have gained popularity. SNNs extend artificial neural networks (ANNs) by encoding information in time as either rates or delays between spiking events, shared between neurons via their weighted connections. SNN-based platforms are event-driven, resulting in naturally sparse, noise-tolerant and power-efficient computation. In this tutorial, we present the state-of-the-art in scalable digital and analog spiking neuromorphic system architectures, and discuss current research trends within the neuromorphic architecture field at the system level. We further introduce our SANA-FE tool for simulation of Advanced Neuromorphic Architectures for Fast Exploration, which has been developed as part of a collaboration between the University of Texas at Austin and Sandia National Laboratories. SANA-FE allows for modeling and performance-power prediction of different spiking hardware architectures executing SNN applications to support rapid, early system-level design-space exploration, hardware-aware application development and system architecture co-design. The tutorial includes a hands-on component in which SANA-FE’s capabilities are demonstrated and used to perform system design and application mapping case studies.

关键词： Neuromorphic engineering Neurons Systems architecture Computer architecture Tutorials Spiking neural networks Predictive models Market research System analysis and design optimization

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A Data-driven Probabilistic Power Flow Analysis Considering Voltage-Dependent Loads

A Data-Driven Probabilistic Power Flow Analysis Considering ...

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International conference on Environment and Electrical Engineering (EEEIC)

作者： Seyed Farhad Zandrazavi Miadreza Shafie-khah Meysam Pashaei Nataly Bañol Arias Thiago Batista Soeiro School of Technology and Innovations University of Vaasa Vaasa Finland Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands

ISBN: (数字)9798350355185

ISBN: (纸本)9798350355192

Probabilistic power flow analysis (PPFA) stands as a promising method for assessing the steady-state performance of distribution networks (DNs) amidst uncertainties associated with renewable energy sources, particularly wind power units (WPUs). However, the reliability of the PPFA results hinges significantly on the accuracy of the power flow model. This paper proposes a new approach to PPFA that integrates voltage-dependent load (VDL). Although incorporating VDL in PPFA formulation enhances the precision of the model, it introduces additional computational complexity due to the introduction of new nonlinear terms into the optimization problem. Therefore, initially, a dataset of wind speed measurements is fitted to a Weibull probability distribution function (PDF). Subsequently, a new nonlinear model is developed, which integrates Monte Carlo (MC) simulation along with the specified PDF for PPFA, accounting for VDL effects. Finally, the proposed model is efficiently convexified using Newton's generalized binomial theorem, piecewise linearization, and appropriate approximations to extract the corresponding linear programming (LP) model. This LP model is then tested on a modified WPU-integrated 33-bus DN, revealing that the inclusion of VDL significantly influences the PPFA results. A comparative analysis between PPFA models with and without VDL incorporation illustrates that overlooking VDL can lead to underestimation of power losses and voltage drops in the analysis.

关键词： Renewable energy sources Accuracy Computational modeling Wind speed Distribution networks Probability density function Probabilistic logic Steady-state Load modeling Load flow analysis

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