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检索条件"机构=Institute for Scientific Computing and Optimization"
28 条 记 录,以下是1-10 订阅
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Who Should I Trust? A Visual Analytics Approach for Comparing Net Load Forecasting Models
Who Should I Trust? A Visual Analytics Approach for Comparin...
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2025 IEEE PES Grid Edge Technologies Conference and Exposition, Grid Edge 2025
作者: Bhattacharjee, Kaustav Kundu, Soumya Chakraborty, Indrasis Dasgupta, Aritra New Jersey Institute of Technology Department of Data Science United States Pacific Northwest National Laboratory Optimization and Control Group United States Lawrence Livermore National Laboratory Center for Applied Scientific Computing United States
Net load forecasting is crucial for energy planning and facilitating informed decision-making regarding trade and load distributions. However, evaluating forecasting models' performance against benchmark models re... 详细信息
来源: 评论
Forte: An Interactive Visual Analytic Tool for Trust-Augmented Net Load Forecasting
Forte: An Interactive Visual Analytic Tool for Trust-Augment...
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2024 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2024
作者: Bhattacharjee, Kaustav Kundu, Soumya Chakraborty, Indrasis Dasgupta, Aritra New Jersey Institute of Technology Department of Data Science United States Pacific Northwest National Laboratory Optimization and Control Group United States Lawrence Livermore National Laboratory Center for Applied Scientific Computing United States
Accurate net load forecasting is vital for energy planning, aiding decisions on trade and load distribution. However, assessing the performance of forecasting models across diverse input variables, like temperature an... 详细信息
来源: 评论
Who Should I Trust? A Visual Analytics Approach for Comparing Net Load Forecasting Models
Who Should I Trust? A Visual Analytics Approach for Comparin...
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Grid Edge Technologies Conference & Exposition (Grid Edge), IEEE PES
作者: Kaustav Bhattacharjee Soumya Kundu Indrasis Chakraborty Aritra Dasgupta Department of Data Science New Jersey Institute of Technology USA Optimization and Control Group Pacific Northwest National Laboratory USA Center for Applied Scientific Computing Lawrence Livermore National Laboratory USA
Net load forecasting is crucial for energy planning and facilitating informed decision-making regarding trade and load distributions. However, evaluating forecasting models' performance against benchmark models re... 详细信息
来源: 评论
Forte: An Interactive Visual Analytic Tool for Trust-Augmented Net Load Forecasting
Forte: An Interactive Visual Analytic Tool for Trust-Augment...
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Innovative Smart Grid Technologies (ISGT)
作者: Kaustav Bhattacharjee Soumya Kundu Indrasis Chakraborty Aritra Dasgupta Department of Data Science New Jersey Institute of Technology USA Optimization and Control Group Pacific Northwest National Laboratory USA Center for Applied Scientific Computing Lawrence Livermore National Laboratory USA
Accurate net load forecasting is vital for energy planning, aiding decisions on trade and load distribution. However, assessing the performance of forecasting models across diverse input variables, like temperature an...
来源: 评论
Who should I trust? A Visual Analytics Approach for Comparing Net Load Forecasting Models
arXiv
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arXiv 2024年
作者: Bhattacharjee, Kaustav Kundu, Soumya Chakraborty, Indrasis Dasgupta, Aritra Department of Data Science New Jersey Institute of Technology United States Optimization and Control Group Pacific Northwest National Laboratory United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory United States
Net load forecasting is crucial for energy planning and facilitating informed decision-making regarding trade and load distributions. However, evaluating forecasting models' performance against benchmark models re... 详细信息
来源: 评论
Forte: An Interactive Visual Analytic Tool for Trust-Augmented Net Load Forecasting
arXiv
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arXiv 2023年
作者: Bhattacharjee, Kaustav Kundu, Soumya Chakraborty, Indrasis Dasgupta, Aritra Department of Data Science New Jersey Institute of Technology United States Optimization and Control Group Pacific Northwest National Laboratory United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory United States
Accurate net load forecasting is vital for energy planning, aiding decisions on trade and load distribution. However, assessing the performance of forecasting models across diverse input variables, like temperature an... 详细信息
来源: 评论
Quality Control in Particle Precipitation via Robust optimization
arXiv
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arXiv 2023年
作者: Kuchlbauer, Martina Dienstbier, Jana Muneer, Adeel Hedges, Hanna Stingl, Michael Liers, Frauke Pflug, Lukas Optimization under Uncertainty and Data Analysis Departement of Data Science Cauerstraße 11 Erlangen91058 Germany Cauerstraße 11 Erlangen91058 Germany Central Institute for Scientific Computing Martensstr. 5a Erlangen91058 Germany
We propose a robust optimization approach to mitigate the impact of uncertainties in particle precipitation. Our model of particle synthesis incorporates, as partial differential equations, nonlinear and nonlocal popu... 详细信息
来源: 评论
Kernel Neural Operators (KNOs) for Scalable, Memory-efficient, Geometrically-flexible Operator Learning
arXiv
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arXiv 2024年
作者: Lowery, Matthew Turnage, John Morrow, Zachary Jakeman, John D. Narayan, Akil Zhe, Shandian Shankar, Varun Kahlert School of Computing University of Utah UT United States Department of Mathematics University of Utah UT United States Scientific Machine Learning Sandia National Laboratories United States Optimization and Uncertainty Quantification Sandia National Laboratories United States Institute Department of Mathematics University of Utah UT United States
This paper introduces the Kernel Neural Operator (KNO), a novel operator learning technique that uses deep kernel-based integral operators in conjunction with quadrature for function-space approximation of operators (... 详细信息
来源: 评论
Enhanced prairie dog optimization with Levy flight and dynamic opposition-based learning for global optimization and engineering design problems
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Neural computing and Applications 2024年 第19期36卷 11137-11170页
作者: Biswas, Saptadeep Shaikh, Azharuddin Ezugwu, Absalom El-Shamir Greeff, Japie Mirjalili, Seyedali Bera, Uttam Kumar Abualigah, Laith Department of Mathematics National Institute of Technology Agartala Tripura Agartala India Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom2520 South Africa Faculty of Natural and Agricultural Sciences School of Computer Science and Information Systems North-West University Vanderbijlpark South Africa Centre for Artificial Intelligence Research and Optimization Torrens University Australia Fortitude Valley BrisbaneQLD4006 Australia Research Center University of Tabuk Tabuk71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia College of Engineering Yuan Ze University Taoyuan32003 Taiwan MEU Research Unit Middle East University Amman11831 Jordan
This study proposes a new prairie dog optimization algorithm version called EPDO. This new version aims to address the issues of premature convergence and slow convergence that were observed in the original PDO algori... 详细信息
来源: 评论
Democratizing Uncertainty Quantification
arXiv
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arXiv 2024年
作者: Seelinger, Linus Reinarz, Anne Lykkegaard, Mikkel B. Akers, Robert Alghamdi, Amal M.A. Aristoff, David Bangerth, Wolfgang Bénézech, Jean Diez, Matteo Frey, Kurt Jakeman, John D. Jørgensen, Jakob S. Kim, Ki-Tae Kent, Benjamin M. Martinelli, Massimiliano Parno, Matthew Pellegrini, Riccardo Petra, Noemi Riis, Nicolai A.B. Rosenfeld, Katherine Serani, Andrea Tamellini, Lorenzo Villa, Umberto Dodwell, Tim J. Scheichl, Robert Scientific Computing Center Karlsruhe Institute of Technology Karlsruhe Germany Department of Computer Science Durham University Durham United Kingdom digiLab Exeter United Kingdom UK Atomic Energy Authority Oxford United Kingdom Lyngby Denmark Department of Mathematics Colorado State University Fort CollinsCO United States Department of Mathematics Department of Geosciences Colorado State University Fort CollinsCO United States Centre for Integrated Materials Processes and Structures Department of Mechanical Engineering University of Bath Bath United Kingdom National Research Council Institute of Marine Engineering Rome Italy Institute for Disease Modeling Global Health Division Bill & Melinda Gates Foundation United States Optimization and Uncertainty Quantification Sandia National Laboratories AlbuquerqueNM United States University of California Merced United States National Research Council-Institute for Applied Mathematics and Information Technologies "E. Magenes" Pavia Italy Solea Energy ThetfordVT United States Copenhagen Imaging ApS Herlev Denmark The University of Texas Austin United States Heidelberg University Germany
Uncertainty Quantification (UQ) is vital to safety-critical model-based analyses, but the widespread adoption of sophisticated UQ methods is limited by technical complexity. In this paper, we introduce UM-Bridge (the ... 详细信息
来源: 评论