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SSRN 2024年

作者： Kong, Lingping Velasquez, Juan D. Perfilieva, Irina Pant, Millie Pan, Jeng-Shyang Snášel, Václav Department of Computer Science VSB-Technical University of Ostrava Ostrava Czech Republic Department of Industrial Engineering University of Chile Santiago Chile Santiago Chile Institute for Research and Applications of Fuzzy Modeling Centre of Excellence IT4Innovations division of The University of Ostrava Ostrava Czech Republic Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Uttarakhand Roorkee247667 India School of Artificial Intelligence Nanjing University of Information Science and Technology China Instituto Sistemas Complejos de Ingeniería Santiago Chile Santiago Chile Faculty of Computer Science and Telecommunications Cracow University of Technology Kraków Poland Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Uttarakhand Roorkee247667 India College of Computer Science and Engineering Shandong University of Science and Technology China

Within the realm of data mining and machine learning, feature selection stands out as an essential technique for dimensionality reduction, presenting both popularity and complexity. Feature selection involves a nuanced trade-off that results in information loss. This distinction underscores the necessity for the development of lightweight, efficient, and practical methods capable of minimizing information loss while executing dimensional reduction. In this paper, we introduce a rankability-based feature selection method. The concept of rankability, proposed in 2019 and similar to the entropy concept, remains relatively unexplored. Our proposed method distinguishes itself by its lightweight complexity, eliminating the need for iterative *** evaluate the effectiveness of our approach, we conducted experiments on forty-one datasets and compared the results with four other algorithms using refined data with 10\%, 30\%, and 50\% feature dimension reduction. The findings reveal that our rankability-based feature selection method consistently outperforms the other four methods across a majority of datasets in terms of accuracy, precision, recall, and F1-macro performance. Specifically, the classification score exceeds 0.9 on accuracy for 50\% feature dimension reduction in twelve datasets, with nine datasets achieving scores over 0.9 in recall, F1-macro, and precision. Overall, our proposed feature selection method demonstrates promising performance. © 2024, The Authors. All rights reserved.

关键词： Feature Selection

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First-principles investigation of the solid-solid interface energy of bcc Ti/hcp Ti

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AIP Conference Proceedings 2024年第1期2995卷

作者： Ankosh D. Deshmukh Arjun R. Varma M. P. Gururajan Vaishali Shah Department of Scientific Computing Modeling and Simulation Savitribai Phule Pune University Pune 411007 India Department of Physics Savitribai Phule Pune University Pune 411007 India Department of Metallurgical Engineering and Materials Science Indian Institute of Technology Bombay Powai Mumbai Maharashtra 400076 India

We use first-principles density functional theory (DFT) calculations to obtain solid-solid interface energy of bccTi ( 1¯12)/hcp Ti(11¯00). We find that the interface energy is negative which accounts for the strong chemical bonding and mixing of atoms in the interfacial region leading to spontaneous phase transformation. The lower interface energy for the hcp Ti/bcc Ti interface than that reported for other fcc Fe/hcp Fe and Ti/TiX(X=C, N) interfaces in the literature gives this interface a much better stability.

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Ab-initio simulations of Ni and Ni0.5Cu0.5 generalized stacking fault energies

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AIP Conference Proceedings 2024年第1期2995卷

作者： Abhishek Kumbhar Ankosh D. Deshmukh R. Arjun Varma M. P. Gururajan Vaishali Shah Department of Scientific Computing Modeling and Simulation Savitribai Phule Pune University Pune 411007 India Department of Physics Savitribai Phule Pune University Pune 411007 India Department of Metallurgical Engineering and Materials Science Indian Institute of Technology Bombay Powai Mumbai Maharashtra 400076 India

We performed ab-initio calculations based on density functional theory (DFT) for obtaining the generalized stacking fault energies (GSFEs) and elastic moduli of Cu, magnetic and non-magnetic Ni and Ni0.5Cu0.5 alloy. We have studied the effect of magnetism on the elastic constants and GSFEs in 50% Cu doped Ni alloy. The GSFEs and elastic constants show a decrease upon Cu doping in agreement with the trends in theoretical and experimental results for Ni-rich alloy of Ni-Cu. The Rice criteria [1] analysis showed that adding Cu to Ni reduces the ductility of Ni which may lead to an increase in the strength of alloy compared to pure Cu and Ni.

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Design decision support system for the engineering/re-engineering of complex naval systems

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NAVAL ENGINEERS JOURNAL 1997年第4期109卷 23-33页

作者： Graham, JH Ragade, RK Shea, J James H. Graham:is the Henry Vogt Professor of computer science and engineering at the University of Louisville. Previous professional employment includes positions at Rensselaer Polytechnic Institute and General Motors Corporation. He received his master's and doctoral degrees at Purdue University and his bachelor's degree at Rose-Hulman Institute of Ethnology. His research interests include artificial intelligence robotics and paraliel computing. Rammohan K. Ragade:is professor of engineering mathematics and computer science at the University of Louisville. He received his Ph.D. from the Indian Institute of Technology at Kampur in 1968. He has been a faculty member at the University of Waterloo and held an engineering position with Bell Northern Research. His research interests include human-computer interaction decision support systems object-oriented simulation and modeling distributed database systems and fuzzy logic. John G. Shea Jr.:served until 1995 as a project manager in the Phalanx Engineering Section of Naval Surface Warfare Center Crane Division Louisville Station. He received his bachelor's and master's degrees from the Speed Scientific School at the University of Louisville. He was a member of the American Society of Naval Engineers.

Hybrid simulation includes both functional computer software models and actual hardware subsystems operating as an integrated whole. A ''hot test bed'' or a ''hybrid simulator-emulator'' enables design engineers to evaluate conceptual models in a more realistic real-lime context, but places increased cognitive demands on the designer. This paper describes an architecture, and results from a prototype implementation, for a design decision support system to allow a designer to make more productive and cost effective use of a test bed system.

关键词： Decision support systems

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Electrospun nanofibrous membrane for electrodialysis

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Journal of Physics: Conference Series 2019年第1期1209卷

作者： J X Jiang G F Zheng X Wang G Fu J Liu W W Li M F Zhuang SH M Guo Department of Instrumental and Electrical Engineering Xiamen University Xiamen 361102 China School of Mechanical and Manufacturing Engineering Xiamen Institute of Technology Xiamen 361024 China School of Mathematical Sciences Xiamen University Xiamen 361021 China Fujian Provincial Key Laboratory of Mathematical Modeling and High Performance Scientific Computing School of Mathematical Sciences Xiamen University Xiamen 361005 China

The direct fabrication of homogenous membrane for electrodialysis has become the research hotspots in the field of water purification. A novel multi-jet electrospinning method with sheath gas was presented to study the fabrication of homogenous electrodialysis membrane. By soaking into sulfuric acid, the reactive exchange groups can be introduced into the electrospun nanofibrous membrane that has been hot pressed. Thanks to the pores among nanofibers, electrospun electrodialysis membrane had advantages of higher porosity, higher water uptake and lower membrane resistance, which promoted the ion transmission through the membrane. The porosity, water uptake and ion exchange capacity (IEC) reduced with the increase of membrane thickness. An electrodialysis separation system was set up to test its separation performance. The results showed that the desalinization ratio of NaCl solution could be reached to 58.15% within 30 minutes. The electrospun nanofibrous membrane for electrodialysis provides a simple and effective way to expand its industrial application.

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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 UQ and modeling Bridge), a high-level abstraction and software protocol that facilitates universal interoperability of UQ software with simulation codes. It breaks down the technical complexity of advanced UQ applications and enables separation of concerns between experts. UM-Bridge democratizes UQ by allowing effective interdisciplinary collaboration, accelerating the development of advanced UQ methods, and making it easy to perform UQ analyses from prototype to High Performance computing (HPC) scale. In addition, we present a library of ready-to-run UQ benchmark problems, all easily accessible through UM-Bridge. These benchmarks support UQ methodology research, enabling reproducible performance comparisons. We demonstrate UM-Bridge with several scientific applications, harnessing HPC resources even using UQ codes not designed with HPC support. © 2024, CC BY-NC-ND.

关键词： Application programs

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sbi reloaded: a toolkit for simulation-based inference workflows

arXiv

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arXiv 2024年

作者： Boelts, Jan Deistler, Michael Gloeckler, Manuel Tejero-Cantero, Álvaro Lueckmann, Jan-Matthis Moss, Guy Steinbach, Peter Moreau, Thomas Muratore, Fabio Linhart, Julia Durkan, Conor Vetter, Julius Miller, Benjamin Kurt Herold, Maternus Ziaeemehr, Abolfazl Pals, Matthijs Gruner, Theo Bischoff, Sebastian Krouglova, Anastasia N. Gao, Richard Lappalainen, Janne K. Mucsányi, Bálint Pei, Felix Schulz, Auguste Stefanidi, Zinovia Rodrigues, Pedro L.C. Schröder, Cornelius Zaid, Faried Abu Beck, Jonas Kapoor, Jaivardhan Greenberg, David S. Gonçalves, Pedro J. Macke, Jakob H. Machine Learning in Science University of Tübingen Germany Tübingen AI Center Germany TransferLab AppliedAI Institute for Europe Germany ML Colab Cluster ML in Science University of Tübingen Germany Google Research United States Helmholtz-Zentrum Dresden-Rossendorf Germany Université Paris-Saclay INRIA CEA Palaiseau France Robert Bosch GmbH Germany School of Informatics University of Edinburgh United Kingdom University of Amsterdam Netherlands Research and Innovation Center BMW Group Germany Institute for Applied Mathematics and Scientific Computing University of the Bundeswehr Munich Germany Aix Marseille INSERM INS France TU Darmstadt Hessian.AI Germany University Hospital Tübingen M3 Research Center Germany Faculty of Science KU Leuven B-3000 Belgium Imec Belgium Methods of Machine Learning University of Tübingen Germany Neuroscience Institute Carnegie Mellon University United States Université Grenoble Alpes INRIA CNRS Grenoble INP LJK France Hertie Institute for AI in Brain Health University of Tübingen Germany Institute of Coastal Systems - Analysis and Modeling Helmholtz AI Germany Departments of Computer Science Electrical Engineering KU Leuven Belgium Department Empirical Inference Max Planck Institute for Intelligent Systems Tübingen Germany

Scientists and engineers use simulators to model empirically observed phenomena. However, tuning the parameters of a simulator to ensure its outputs match observed data presents a significant challenge. Simulation-based inference (SBI) addresses this by enabling Bayesian inference for simulators, identifying parameters that match observed data and align with prior knowledge. Unlike traditional Bayesian inference, SBI only needs access to simulations from the model and does not require evaluations of the likelihood-function. In addition, SBI algorithms do not require gradients through the simulator, allow for massive parallelization of simulations, and can perform inference for different observations without further simulations or training, thereby amortizing inference. Over the past years, we have developed, maintained, and extended sbi, a PyTorch-based package4 that implements Bayesian SBI algorithms based on neural networks. The sbi toolkit implements a wide range of inference methods, neural network architectures, sampling methods, and diagnostic tools. In addition, it provides well-tested default settings but also offers flexibility to fully customize every step of the simulation-based inference workflow. Taken together, the sbi toolkit enables scientists and engineers to apply state-of-the-art SBI methods to black-box simulators, opening up new possibilities for aligning simulations with empirically observed data. © 2024, CC BY-SA.

关键词： Neural networks

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Verification benchmarks for single-phase flow in three-dimensional fractured porous media

arXiv

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arXiv 2020年

作者： Berre, Inga Boon, Wietse M. Flemisch, Bernd Fumagalli, Alessio Gläser, Dennis Keilegavlen, Eirik Scotti, Anna Stefansson, Ivar Tatomir, Alexandru Brenner, Konstantin Burbulla, Samuel Devloo, Philippe Duran, Omar Favino, Marco Hennicker, Julian Lee, I-Hsien Lipnikov, Konstantin Masson, Roland Mosthaf, Klaus Chiara Nestola, Maria Giuseppina Ni, Chuen-Fa Nikitin, Kirill Schädle, Philipp Svyatskiy, Daniil Yanbarisov, Ruslan Zulianp, Patrick Department of Mathematics University of Bergen Allégaten 41 Bergen5007 Norway Department of Mathematics KTH Royal Institute of Technology Lindstedtsvägen 25 Stockholm11428 Sweden Department of Hydromechanics and Modelling of Hydrosystems University of Stuttgart Pfaffenwaldring 61 Stuttgart70569 Germany Laboratory for Modeling and Scientific Computing MOX Politecnico di Milano p.za Leonardo da Vinci 32 Milano20133 Italy Department of Applied Geology Geosciences Center University of Göttingen Goldschmidtstrasse 3 Göttingen37077 Germany Department of Earth Sciences Uppsala University Villavägen 16 UppsalaS-75236 Sweden University of Côte d’Azur CNRS INRIA LJAD Nice France Institute of Applied Analysis and Numerical Simulation University of Stuttgart Pfaffenwaldring 57 Stuttgart70569 Germany FEC Universidade Estadual de Campinas Cidade Universitária R. Josiah Willard Gibbs 85 Campinas-SPCEP 13083-839 Brazil Institute of Earth Sciences University of Lausanne Building Geopolis UNIL-Mouline Lausanne1015 Switzerland Section de Mathématiques Université de Genève 2-4 rue du Lièvre CP 64 Genève1211 Switzerland Graduate Institute of Applied Geology National Central University Taiwan Center for Environmental Studies National Central University Taiwan Los Alamos National Laboratory New Mexico United States Department of Environmental Engineering Technical University of Denmark Bygningstorvet Building 115 Kgs. Lyngby2800 Denmark Numerical Simulation in Science Medicine and Engineering Group Institute of Computational Science Università della Svizzera italiana. Via G. Buffi 13 Lugano Ticino6900 Switzerland Marchuk Institute of Numerical Mathematics of Russian Academy of Sciences Moscow Russia ETH Zürich Geothermal Energy and Geofluids Group Institute of Geophysics Zürich8092 Switzerland

Flow in fractured porous media occurs in the earth’s subsurface, in biological tissues, and in man-made materials. Fractures have a dominating influence on flow processes, and the last decade has seen an extensive development of models and numerical methods that explicitly account for their presence. To support these developments, we present a portfolio of four benchmark cases for single-phase flow in three-dimensional fractured porous media. The cases are specifically designed to test the methods’ capabilities in handling various complexities common to the geometrical structures of fracture networks. Based on an open call for participation, results obtained with 17 numerical methods were collected. This paper presents the underlying mathematical model, an overview of the features of the participating numerical methods, and their performance in solving the benchmark cases. Copyright © 2020, The Authors. All rights reserved.

关键词： Numerical methods

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INTRODUCTION

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Bulletin of the American Meteorological Society 2024年第8期105卷 S1-S11页

作者： Boyer, T. Blunden, J. Dunn, R.J.H. NOAA/NESDIS National Centers for Environmental Information Silver Spring Maryland NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Met Office Hadley Centre Exeter United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland Scripps Institution of Oceanography University of California San Diego La Jolla California NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center College Park Maryland Brandenburg University of Technology (BTU) Cottbus-Senftenberg Germany Servicio Meteorológico Nacional Buenos Aires Argentina NOAA/OAR Physical Sciences Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Bonn Germany Center for Geophysical Research and School of Physics University of Costa Rica San José Costa Rica Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Centro Nacional de Monitoramento e Alertas de Desastres Naturais CEMADEN São Paulo Brazil Université Grenoble Alpes Institut des Géosciences de l’Environnement IRD CNRS Grenoble INP Grenoble France Hampton University Hampton Virginia Seychelles Meteorological Authority Mahe Seychelles Section for Glaciers Ice and Snow Norwegian Water Resources and Energy Directorate Oslo Norway National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany NOAA Global Monitoring Laboratory Boulder Colorado Servicio Nacional de Meteorología e Hidrología del Perú Lima Perú Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Department o

In 2023, La Niña conditions that generally prevailed in the eastern Pacific Ocean from mid-2020 into early 2023 gave way to a strong El Niño by October. Atmospheric concentrations of Earth’s major greenhouse gases—carbon dioxide, methane, and nitrous oxide—all increased to record-high levels. The annual global average carbon dioxide concentration in the atmosphere rose to 419.3±0.1 ppm, which is 50% greater than the pre-industrial level. The growth from 2022 to 2023 was 2.8 ppm, the fourth highest in the record since the 1960s. The combined short-term effects of El Niño and the long-term effects of increasing levels of heat-trapping gases in the atmosphere contributed to new records for many essential climate variables reported here. The annual global temperature across land and oceans was the highest in records dating as far back as 1850, with the last seven months (June–December) having each been record warm. Over land, the globally averaged temperature was also record high. Dozens of countries reported record or near-record warmth for the year, including China and continental Europe as a whole (warmest on record), India and Russia (second warmest), and Canada (third warmest). Intense and widespread heatwaves were reported around the world. In Vietnam, an all-time national maximum temperature record of 44.2°C was observed at Tuong Duong on 7 May, surpassing the previous record of 43.4°C at Huong Khe on 20 April 2019. In Brazil, the air temperature reached 44.8°C in Araçuaí in Minas Gerais on 20 November, potentially a new national record and 12.8°C above normal. The effect of rising temperatures was apparent in the cryosphere, where snow cover extent by June 2023 was the smallest in the 56-year record for North America and seventh smallest for the Northern Hemisphere overall. Heatwaves contributed to the greatest average mass balance loss for Alpine glaciers around the world since the start of the record in 1970. Due to rapid volume loss beginning in 2021, St. A

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11th German Conference on Chemoinformatics (GCC 2015) Abstracts

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JOURNAL OF CHEMINFORMATICS 2016年第1-Sup期8卷 1-27页

作者： [Anonymous] GDCh-CIC Division Associated Board Member Beilstein-Institut zur Förderung der Chemischen Wissenschaften Trakehner Str. 7–9 60487 Frankfurt Germany Division Medicinal Chemistry Amsterdam Institute for Molecules Medicines and Systems (AIMMS) VU University Amsterdam The Netherlands Centre for Bioinformatics Uni Hamburg Bundesstr. 43 20146 Hamburg Germany Sanofi-Aventis Deutschland GmbH 65926 Frankfurt am Main Germany GlaxoSmithKline Stevenage SG1 2NY UK Discngine Paris 75011 France Organisch-Chemisches Institut Westfälische Wilhelms-Universität Münster Germany Bundeskriminalamt Wiesbaden Central Analytics II 65173 Wiesbaden Germany Department of Chemistry and Biochemistry University of California Santa Barbara CA 93111 USA Center for Drug Research Ludwig-Maximilians University Munich Munich Germany Inorganic Chemistry and Center for Nanointegration University of Duisburg-Essen Essen Germany CAM-D Technologies Essen Germany Institute for Bioinformatics and Chemoinformatics Westphalian University of Applied Sciences Recklinghausen Germany Leiden University Leiden Netherlands Culgi BV Berlin Germany Physikalische Chemie III TU Dortmund 44227 Dortmund Germany Centre for Molecular Informatics Department of Chemistry University of Cambridge Lensfield Road Cambridge CB2 1EW United Kingdom Unilever Centre for Molecular Informatics Department of Chemistry Lensfield Road Cambridge CB2 1EW UK Centre for Molecular Science Informatics Department of Chemistry University of Cambridge Cambridge CB2 1EW UK Department of Chemistry Bar-Ilan University Ramat-Gan 5290002 Israel Department of Pharmaceutical Chemistry University of Marburg Marburg Germany Pharmaceutical and Medicinal Chemistry Institute of Pharmacy Freie Universität Berlin Königin-Luise Straße 2+4 14195 Berlin Germany Computer-Aided Drug Design Institute of Pharmacy Freie Universität Berlin 14195 Berlin Germany Center for Bioinformatics University of Hamburg 20146 Hamburg Germany Department of P

Information about several papers discussed at the 11th German Conference on Chemoinformatics (GCC) held November 8-10, 2015 sponsored by the Chemistry-Information-Computer (CIC) division of the German Chemical Society is presented. Topics include accurate description of protein-ligand binding and its functional activity, combination of chemoinformatics and bioinformatics using three dimensional structure of amino acids, and Human Genome Project leading to advancement in drug discovery.

关键词： CHEMINFORMATICS PREDICTION models MONTE Carlo method

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