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arXiv 2023年

作者： Zeng, Jinzhe Zhang, Duo Lu, Denghui Mo, Pinghui Li, Zeyu Chen, Yixiao Rynik, Marián Huang, Li'ang Li, Ziyao Shi, Shaochen Wang, Yingze Ye, Haotian Tuo, Ping Yang, Jiabin Ding, Ye Li, Yifan Tisi, Davide Zeng, Qiyu Bao, Han Xia, Yu Huang, Jiameng Muraoka, Koki Wang, Yibo Chang, Junhan Yuan, Fengbo Bore, Sigbjørn Løland Cai, Chun Lin, Yinnian Wang, Bo Xu, Jiayan Zhu, Jia-Xin Luo, Chenxing Zhang, Yuzhi Goodall, Rhys E.A. Liang, Wenshuo Singh, Anurag Kumar Yao, Sikai Zhang, Jingchao Wentzcovitch, Renata Han, Jiequn Liu, Jie Jia, Weile York, Darrin M. Weinan, E. Car, Roberto Zhang, Linfeng Wang, Han Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China College of Electrical and Information Engineering Hunan University Changsha China Yuanpei College Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States Department of Experimental Physics Comenius University Mlynská Dolina F2 Bratislava842 48 Slovakia Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Center for Data Science Peking University Beijing100871 China ByteDance Research Zhonghang Plaza No. 43 North 3rd Ring West Road Haidian District Beijing China College of Chemistry and Molecular Engineering Peking University Beijing100871 China Baidu Inc. Beijing China Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University Zhejiang Hangzhou China Westlake AI Therapeutics Lab Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Chemistry Princeton University PrincetonNJ08544 United States SISSA Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Laboratory of Computational Science and Modeling Institute of Materials École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Physics National University of Defense Technology Hunan Changsha410073 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineerin

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

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Towards Implementation of the Pressure-Regulated, Feedback-Modulated Model of Star Formation in Cosmological Simulations: Methods and Application to TNG

arXiv

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arXiv 2024年

作者： Hassan, Sultan Ostriker, Eve C. Kim, Chang-Goo Bryan, Greg L. Burger, Jan D. Fielding, Drummond B. Forbes, John C. Genel, Shy Hernquist, Lars Jeffreson, Sarah M.R. Motwani, Bhawna Smith, Matthew C. Somerville, Rachel S. Steinwandel, Ulrich P. Teyssier, Romain Center for Cosmology and Particle Physics Department of Physics New York University 726 Broadway New YorkNY10003 United States Center for Computational Astrophysics Flatiron Institute 162 5th Ave New YorkNY10010 United States Department of Physics & Astronomy University of the Western Cape Cape Town7535 South Africa Department of Astrophysical Sciences Princeton University PrincetonNJ08544 United States Institute for Advanced Study 1 Einstein Drive PrincetonNJ08540 United States Department of Astronomy Columbia University 550 W 120th Street New YorkNY10027 United States Max-Planck-Institut für Astrophysik Karl-Schwarzschild-Str. 1 GarchingD-85748 Germany Department of Astronomy Cornell University IthacaNY14853 United States School of Physical and Chemical Sciences-Te Kura Matū University of Canterbury Private Bag 4800 Christchurch8140 New Zealand Columbia Astrophysics Laboratory Columbia University 550 West 120th Street New YorkNY10027 United States Center for Astrophysics Harvard & Smithsonian 60 Garden Street CambridgeMA United States Program in Applied and Computational Mathematics Princeton University Fine Hall Washington Road PrincetonNJ08544-1000 United States

Traditional star formation subgrid models implemented in cosmological galaxy formation simulations, such as that of Springel & Hernquist (2003, hereafter SH03), employ adjustable parameters to satisfy constraints measured in the local Universe. In recent years, however, theory and spatially-resolved simulations of the turbulent, multiphase, star-forming ISM have begun to produce new first-principles models, which when fully developed can replace traditional subgrid prescriptions. This approach has advantages of being physically motivated and predictive rather than empirically tuned, and allowing for varying environmental conditions rather than being tied to local Universe conditions. As a prototype of this new approach, by combining calibrations from the TIGRESS numerical framework with the Pressure-Regulated Feedback-Modulated (PRFM) theory, simple formulae can be obtained for both the gas depletion time and an effective equation of state. Considering galaxies in TNG50, we compare the "native" simulation outputs with post-processed predictions from PRFM. At TNG50 resolution, the total midplane pressure is nearly equal to the total ISM weight, indicating that galaxies in TNG50 are close to satisfying vertical equilibrium. The measured gas scale height is also close to theoretical equilibrium predictions. The slopes of the effective equations of states are similar, but with effective velocity dispersion normalization from SH03 slightly larger than that from current TIGRESS simulations. Because of this and the decrease in PRFM feedback yield at high pressure, the PRFM model predicts shorter gas depletion times than the SH03 model at high densities and redshift. Our results represent a first step towards implementing new, numerically calibrated subgrid algorithms in cosmological galaxy formation simulations. Copyright © 2024, The Authors. All rights reserved.

关键词： Cosmology

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26th Annual computational Neuroscience Meeting (CNS*2017) of the Organization for computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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Gamma-ray Bursts as Distance Indicators by a Statistical Learning Approach

arXiv

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arXiv 2024年

作者： Dainotti, Maria Giovanna Narendra, Aditya Pollo, Agnieszka Petrosian, Vahé Bogdan, Malgorzata Iwasaki, Kazunari Prochaska, Jason Xavier Rinaldi, Enrico Zhou, David National Astronomical Observatory of Japan Mitaka Tokyo181-8588 Japan The Graduate University for Advanced Studies SOKENDAI Kanagawa240-0193 Japan Space Science Institute BoulderCO80301 United States Doctoral School of Exact and Natural Sciences Jagiellonian University Krakow Poland Astronomical Observatory of Jagiellonian University Krakow Poland Warsaw Poland Department of Physics Stanford University 382 Via Pueblo Mall StanfordCA94305-4060 United States Kavli Institute for Particle Astrophysics and Cosmology Stanford University United States Department of Applied Physics Stanford University United States Department of Mathematics University of Wroclaw 50-384 Poland Department of Statistics Lund University LundSE-221 00 Sweden Center for Computational Astrophysics National Astronomical Observatory of Japan 2 Chome-21-1 Osawa Mitaka Tokyo181-8588 Japan University of California Santa Cruz 1156 High Street Santa CruzCA95064 United States Program RIKEN Saitama Wakoshi 351-0198 Japan Arizona State University 1151 S Forest Ave TempeAZ85281 United States

Gamma-ray bursts (GRBs) can be probes of the early universe, but currently, only 26% of GRBs observed by the Neil Gehrels Swift Observatory GRBs have known redshifts (z) due to observational limitations. To address this, we estimated the GRB redshift (distance) via a supervised statistical learning model that uses optical afterglow observed by Swift and ground-based telescopes. The inferred redshifts are strongly correlated (a Pearson coefficient of 0.93) with the observed redshifts, thus proving the reliability of this method. The inferred and observed redshifts allow us to estimate the number of GRBs occurring at a given redshift (GRB rate) to be 8.47-9yr−1Gpc−1 for 1.9 © 2024, CC BY.

关键词： Stars

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Corrigendum: Hyperuniformity variation with quasicrystal local isomorphism class

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Journal of physics. Condensed matter : an Institute of Physics journal 2017年第47期29卷 2017 Aug 8页

作者： C Lin P J Steinhardt S Torquato Department of Physics Princeton University Princeton NJ 08544 United States of America. Princeton Center for Theoretical Science Princeton University Princeton NJ 08544 United States of America. Department of Chemistry Princeton University Princeton NJ 08544 United States of America. Program of Applied and Computational Mathematics Princeton University Princeton NJ 08544 United States of America. Princeton Institute for the Science and Technology of Materials Princeton University Princeton NJ 08544 United States of America.

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Benchmark problems for transcranial ultrasound simulation: Intercomparison of compressional wave models

arXiv

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arXiv 2022年

作者： Aubry, Jean-Francois Bates, Oscar Boehm, Christian Pauly, Kim Butts Christensen, Douglas Cueto, Carlos Gélat, Pierre Guasch, Lluis Jaros, Jiri Jing, Yun Jones, Rebecca Li, Ningrui Marty, Patrick Montanaro, Hazael Neufeld, Esra Pichardo, Samuel Pinton, Gianmarco Pulkkinen, Aki Stanziola, Antonio Thielscher, Axel Treeby, Bradley Van't Wout, Elwin Physics for Medicine Paris INSERM U1273 ESPCI Paris PSL University CNRS UMR 8063 France Department of Bioengineering Imperial College London Exhibition Road LondonSW7 2AZ United Kingdom Institute of Geophysics ETH Zürich Sonneggstrasse 5 Zürich8092 Switzerland Department of Radiology Stanford University StanfordCA United States Department of Biomedical Engineering Department of Electrical and Computer Engineering University of Utah United States Department of Surgical Biotechnology Division of Surgery and Interventional Science University College London LondonNW3 2PF United Kingdom Earth Science and Engineering Department Imperial College London London United Kingdom Centre of Excellence IT4Innovations Faculty of Information Technology Brno University of Technology Bozetechova 2 Brno612 00 Czech Republic Graduate Program in Acoustics The Pennsylvania State University University Park PA16802 United States Joint Department of Biomedical Engineering University of North Carolina at Chapel Hill North Carolina State University NC United States Department of Electrical Engineering Stanford University StanfordCA United States Zurich Switzerland Laboratory for Acoustics / Noise control Empa Swiss Federal Laboratories for Materials Science and Technology Dubendorf Switzerland Zurich Switzerland Radiology and Clinical Neurosciences Departments Cumming School of Medicine University of Calgary Canada Department of Applied Physics University of Eastern Finland Kuopio70211 Finland Department of Medical Physics and Biomedical Engineering University College London Gower Street LondonWC1E 6BT United Kingdom Technical University of Denmark Denmark Danish Research Center for Magnetic Resonance Copenhagen University Hospital Hvidovre Denmark Institute for Mathematical and Computational Engineering School of Engineering and Faculty of Mathematics Pontificia Universidad Catolica de Chile Santiago Chile

computational models of acoustic wave propagation are frequently used in transcranial ultrasound therapy, for example, to calculate the intracranial pressure field or to calculate phase delays to correct for skull distortions. To allow intercomparison between the different modeling tools and techniques used by the community, an international working group was convened to formulate a set of numerical benchmarks. Here, these benchmarks are presented, along with intercomparison results. Nine different benchmarks of increasing geometric complexity are defined. These include a single-layer planar bone immersed in water, a multi-layer bone, and a whole skull. Two transducer configurations are considered (a focused bowl and a plane piston), giving a total of 18 permutations of the benchmarks. Eleven different modeling tools are used to compute the benchmark results. The models span a wide range of numerical techniques, including the finite-difference time-domain method, angular-spectrum method, pseudospectral method, boundary-element method, and spectral-element method. Good agreement is found between the models, particularly for the position, size, and magnitude of the acoustic focus within the skull. When comparing results for each model with every other model in a cross comparison, the median values for each benchmark for the difference in focal pressure and position are less than 10% and 1 mm, respectively. The benchmark definitions, model results, and intercomparison codes are freely available to facilitate further comparisons. Copyright © 2022, The Authors. All rights reserved.

关键词： Bone

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Publisher Correction: Lithospheric foundering and underthrusting imaged beneath Tibet

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Nature communications 2018年第1期9卷 3443页

作者： Min Chen Fenglin Niu Jeroen Tromp Adrian Lenardic Cin-Ty A Lee Wenrong Cao Julia Ribeiro 318 Keith-Wiess Geology Lab Department of Earth Science Rice University MS 126 6100 Main Street Houston TX 77005 USA. Min.Chen@rice.edu. 318 Keith-Wiess Geology Lab Department of Earth Science Rice University MS 126 6100 Main Street Houston TX 77005 USA. State Key Laboratory of Petroleum Resource and Prospecting and Unconventional Natural Gas Institute China University of Petroleum Beijing 102249 China. Department of Geosciences Princeton University Princeton New Jersey 08544 USA. Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA.

The original version of the Supplementary Information associated with this Article contained an error in Supplementary Figure 4 in which the colours on the maps rendered incorrectly. The HTML has been updated to inclu...

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Corrigendum to "Synergetic effect of bismuth vanadate over copolymerized carbon nitride composites for highly efficient photocatalytic H2 and O2 generation" [J. Colloid Interface Sci. 627 (2022) 621-629]

引用

Journal of colloid and interface science 2022年第PT A期628卷 366-367页

作者： Asif Hayat Muhammad Sohail T A Taha Sunil Kumar Baburao Mane Abdullah G Al-Sehemi Ahmed A Al-Ghamdi W I Nawawi Arkom Palamanit Mohammed A Amin Ahmed M Fallatah Zeeshan Ajmal Hamid Ali Wasim Ullah Khan Muhammad Wajid Shah Javid Khan S Wageh State Key Laboratory of Photocatalysis on Energy and Environment College of Chemistry Fuzhou University Fuzhou 350116 China. Yangtze Delta Region Institute (Huzhou) University of Electronic Science and Technology of China Huzhou 313001 China. Physics Department College of Science Jouf University P.O. Box: 2014 Sakaka Saudi Arabia Physics and Engineering Mathematics Department Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt. Department of Chemistry Khaja Bandanawaz University Kalaburagi Karnataka 585104 India. Research Center for Advanced Materials Science (RCAMS) King Khalid University P.O. Box 9004 Abha 61413 Saudi Arabia Department of Chemistry College of Science King Khalid University P.O. Box 9004 Abha 61413 Saudi Arabia. Department of Physics Faculty of Science King Abdulaziz University Jeddah 21589 Saudi Arabia. Faculty of Applied Sciences Universiti Teknologi MARA Cawangan Perlis Arau Perlis 02600 Malaysia. Energy Technology Program Department of Specialized Engineering Faculty of Engineering Prince of Songkla University 15 Karnjanavanich Rd. Hat Yai Songkhla 90110 Thailand. Department of Chemistry College of Science Taif University P.O. Box 11099 Taif 21944 Saudi Arabia. School of Chemistry and Chemical Engineering Northwestern Polytechnical University Xian 710072 China. Multiscale Computational Materials Facility Key Laboratory of Eco-Materials Advanced Technology College of Materials Science and Engineering Fuzhou University Fuzhou 350100 China. State Key Laboratory of Optoelectronic Materials and Technologies School of Materials Science and Engineering Sun Yat-sen University Guangzhou 510275 China. Electronic address: wasimullah89@***. School Of Chemistry Sun Yat-sen University Guangzhou 510275 China. Electronic address: m.wajidshah@***. College of Materials Science and Engineering Hunan University Changsha Hunan 410082 China. Electronic address: javidchemist@yahoo.com.

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Forecasting: theory and practice

arXiv

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arXiv 2020年

作者： Petropoulos, Fotios Apiletti, Daniele Assimakopoulos, Vassilios Babai, Mohamed Zied Barrow, Devon K. Taieb, Souhaib Ben Bergmeir, Christoph Bessa, Ricardo J. Bijak, Jakub Boylan, John E. Browell, Jethro Carnevale, Claudio Castle, Jennifer L. Cirillo, Pasquale Clements, Michael P. Cordeiro, Clara Oliveira, Fernando Luiz Cyrino de Baets, Shari Dokumentov, Alexander Ellison, Joanne Fiszeder, Piotr Franses, Philip Hans Frazier, David T. Gilliland, Michael Gönül, M. Sinan Goodwin, Paul Grossi, Luigi Grushka-Cockayne, Yael Guidolin, Mariangela Guidolin, Massimo Gunter, Ulrich Guo, Xiaojia Guseo, Renato Harvey, Nigel Hendry, David F. Hollyman, Ross Januschowski, Tim Jeon, Jooyoung Jose, Victor Richmond R. Kang, Yanfei Koehler, Anne B. Kolassa, Stephan Kourentzes, Nikolaos Leva, Sonia Li, Feng Litsiou, Konstantia Makridakis, Spyros Martin, Gael M. Martinez, Andrew B. Meeran, Sheik Modis, Theodore Nikolopoulos, Konstantinos Önkal, Dilek Paccagnini, Alessia Panagiotelis, Anastasios Panapakidis, Ioannis Pavía, Jose M. Pedio, Manuela Pedregal, Diego J. Pinson, Pierre Ramos, Patrícia Rapach, David E. Reade, J. James Rostami-Tabar, Bahman Rubaszek, Michal Sermpinis, Georgios Shang, Han Lin Spiliotis, Evangelos Syntetos, Aris A. Talagala, Priyanga Dilini Talagala, Thiyanga S. Tashman, Len Thomakos, Dimitrios Thorarinsdottir, Thordis Todini, Ezio Arenas, Juan Ramón Trapero Wang, Xiaoqian Winkler, Robert L. Yusupova, Alisa Ziel, Florian School of Management University of Bath United Kingdom Politecnico di Torino Turin Italy Forecasting and Strategy Unit School of Electrical and Computer Engineering National Technical University of Athens Greece Kedge Business School France Department of Management Birmingham Business School University of Birmingham United Kingdom Belgium Faculty of Information Technology Monash University Melbourne Australia INESC TEC – Institute for Systems and Computer Engineering Technology and Science Porto Portugal Department of Social Statistics and Demography University of Southampton United Kingdom Centre for Marketing Analytics and Forecasting Lancaster University Management School Lancaster University United Kingdom School of Mathematics and Statistics University of Glasgow United Kingdom Department of Mechanical and Industrial Engineering University of Brescia Italy Magdalen College University of Oxford United Kingdom ZHAW School of Management and Law Zurich University of Applied Sciences Switzerland ICMA Centre Henley Business School University of Reading United Kingdom Faculdade de Ciências e Tecnologia Universidade do Algarve Portugal CEAUL Faculdade de Ciências Universidade de Lisboa Portugal Brazil Department of Business Informatics and Operations Management Faculty of Economics and Business Administration Universiteit Gent Belgium Let’s Forecast Australia Faculty of Economic Sciences and Management Nicolaus Copernicus University in Torun Poland Econometric Institute Erasmus School of Economics Rotterdam Netherlands Department of Econometrics and Business Statistics Monash University Melbourne Australia SAS United States Newcastle Business School Northumbria University Newcastle upon Tyne United Kingdom Department of Statistical Sciences University of Padua Italy Darden School of Business University of Virginia United States Finance Department Bocconi University Baffi-CAREFIN Centre Milan Italy Department of Tourism and Service Management MODUL Un

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases. © 2020, CC BY.

关键词： Forecasting

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SciPy 1.0—Fundamental algorithms for scientific computing in python

arXiv

引用

arXiv 2019年

作者： Virtanen, Pauli Gommers, Ralf Oliphant, Travis E. Haberland, Matt Reddy, Tyler Cournapeau, David Burovski, Evgeni Peterson, Pearu Weckesser, Warren Bright, Jonathan van der Walt, Stéfan J. Brett, Matthew Wilson, Joshua Millman, K. Jarrod Mayorov, Nikolay Nelson, Andrew R.J. Jones, Eric Kern, Robert Larson, Eric Carey, C.J. Polat, Ilhan Feng, Yu Moore, Eric W. VanderPlas, Jake Laxalde, Denis Perktold, Josef Cimrman, Robert Henriksen, Ian Quintero, E.A. Harris, Charles R. Archibald, Anne M. Ribeiro, Antônio H. Pedregosa, Fabian van Mulbregt, Paul University of Jyväskylä University of Jyväskylä FI-40014 Finland Quansight LLC AustinTX United States Ultrasound Imaging Mayo Clinic RochesterMN55902 United States Electrical Engineering Brigham Young University ProvoUT84604 United States Enthought Inc. AustinTX78701 United States Anaconda Inc AustinTX78701 United States BioResource and Agricultural Engineering Department California Polytechnic State University San Luis ObispoCA93407 United States Department of Mathematics University of California Los AngelesCA90025 United States Los Alamos National Laboratory Los AlamosNM87544 United States Independent Researcher Higher School of Economics Myasnitskaya 20 Moscow101000 Russia Independent Researcher Saue Estonia Department of Mechanics and Applied Mathematics Institute of Cybernetics Tallinn Technical University Akadeemia Rd 21 Tallinn Estonia Independent Researcher NY United States Berkeley Institute for Data Science University of California BerkeleyCA94720 United States School of Psychology University of Birmingham Edgbaston BirmighamB15 2TT United Kingdom Division of Biostatistics University of California BerkeleyCA94720 United States WayRay LLC Skolkovo Innovation Center Moscow143026 Russia Australian Nuclear Science and Technology Organisation Locked Bag 2001 Kirrawee DCNSW2232 Australia Institute for Learning and Brain Sciences University of Washington SeattleWA98195 United States College of Information and Computing Sciences University of Massachusetts Amherst AmherstMA01002 United States Berkeley Center for Cosmological Physics University of California BerkeleyCA94720 United States Bruker Biospin Corp. 15 Fortune Drive BillericaMA01821 United States University of Washington SeattleWA98195 United States New Technologies - Research Centre University of West Bohemia Univerzitní 8 Plzeň306 14 Czech Republic Oden Institute for Computational Engineering and Sciences University of Texas at Austin AustinTX7

SciPy is an open source scientific computing library for the Python programming language. SciPy 1.0 was released in late 2017, about 16 years after the original version 0.1 release. SciPy has become a de facto standard for leveraging scientific algorithms in the Python programming language, with more than 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories, and millions of downloads per year. This includes usage of SciPy in almost half of all machine learning projects on GitHub, and usage by high profile projects including LIGO gravitational wave analysis and creation of the first-ever image of a black hole (M87). The library includes functionality spanning clustering, Fourier transforms, integration, interpolation, file I/O, linear algebra, image processing, orthogonal distance regression, minimization algorithms, signal processing, sparse matrix handling, computational geometry, and statistics. In this work, we provide an overview of the capabilities and development practices of the SciPy library and highlight some recent technical developments. Copyright © 2019, The Authors. All rights reserved.

关键词： Open source software

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