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检索条件"机构=The Program of Applied and Computational Mathematics"
1033 条 记 录,以下是461-470 订阅
排序:
Optimized large hyperuniform binary colloidal suspensions in two dimensions
arXiv
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arXiv 2020年
作者: Ma, Zheng Lomba, Enrique Torquato, Salvatore Department of Physics Princeton University PrincetonNJ08544 United States Instituto de Química Física Rocasolano CSIC Calle Serrano 119 MadridE-28006 Spain Department of Chemistry Department of Physics Princeton Institute for the Science and Technology of Materials Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States
The creation of disordered hyperuniform materials with potentially extraordinary optical properties requires a capacity to synthesize large samples that are effectively hyperuniform down to the nanoscale. Motivated by... 详细信息
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On Phase Reduction and Time Period of Noisy Oscillators
On Phase Reduction and Time Period of Noisy Oscillators
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IEEE Annual Conference on Decision and Control
作者: Zahra Aminzare Philip Holmes Vaibhav Srivastava Department of Mathematics University of Iowa IA USA Program in Applied and Computational Mathematics Princeton Neuroscience Institute Princeton University Princeton NJ USA Electrical and Computer Engineering Michigan State University East Lansing MI USA
We study phase reduction for noisy oscillator models by deriving a reduced order stochastic differential equation describing the phase evolution using the first and second order Phase Response Curves (PRCs). We discus... 详细信息
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Causal models on probability spaces
arXiv
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arXiv 2019年
作者: Cabreros, Irineo Storey, John D. Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ08544 United States
We describe the interface between measure theoretic probability and causal inference by constructing causal models on probability spaces within the potential outcomes framework. We find that measure theory provides a ... 详细信息
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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 th... 详细信息
来源: 评论
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
arXiv
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arXiv 2024年
作者: Papamarkou, Theodore Skoularidou, Maria Palla, Konstantina Aitchison, Laurence Arbel, Julyan Dunson, David Filippone, Maurizio Fortuin, Vincent Hennig, Philipp Hernández-Lobato, José Miguel Hubin, Aliaksandr Immer, Alexander Karaletsos, Theofanis Khan, Mohammad Emtiyaz Kristiadi, Agustinus Li, Yingzhen Mandt, Stephan Nemeth, Christopher Osborne, Michael A. Rudner, Tim G.J. Rügamer, David Teh, Yee Whye Welling, Max Wilson, Andrew Gordon Zhang, Ruqi Department of Mathematics The University of Manchester Manchester United Kingdom Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge United States Spotify London United Kingdom Computational Neuroscience Unit University of Bristol Bristol United Kingdom Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University United States Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany Department of Computer Science Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge United Kingdom Department of Mathematics University of Oslo Oslo Norway Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative CA United States Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London United Kingdom Department of Computer Science UC Irvine Irvine United States Department of Mathematics and Statistics Lancaster University Lancaster United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Center for Data Science New York University New York United States Department of Statistics LMU Munich Munich Germany DeepMind London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences Center for Data Science Computer Science Department New York University New York United States Department of Computer Science Purdue University West Lafayette United States
In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective... 详细信息
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Modeling subgrid-scale force and divergence of heat flux of compressible isotropic turbulence by artificial neural network
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Physical Review Fluids 2019年 第10期4卷 104605-104605页
作者: Chenyue Xie Ke Li Chao Ma Jianchun Wang Shenzhen Key Laboratory of Complex Aerospace Flows Center for Complex Flows and Soft Matter Research Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzhen 518055 People's Republic of China Institute of Computational Mathematics and Scientific Engineering Computing Chinese Academy of Sciences Beijing 100190 People's Republic of China The Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA
In this paper, the subgrid-scale (SGS) force and the divergence of SGS heat flux of compressible isotropic turbulence are modeled directly by an artificial neural network (ANN), which serves as a data-driven SGS model... 详细信息
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DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models
arXiv
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arXiv 2019年
作者: Wang, Haidi Chen, Weijie Zeng, Jinzhe Zhang, Linfeng Wang, Han Zhang, Yuzhi Weinan, E. Yuanpei College of Peking University Beijing100871 China School of Electronic Science and Applied Physics Hefei University of Technology Hefei230601 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China School of Chemistry and Molecular Engineering East China Normal University Shanghai200062 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ United States Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Huayuan Road 6 Beijing100088 China Beijing Institute of Big Data Research Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ United States Beijing Institute of Big Data Research Beijing100871 China
In recent years, promising deep learning based interatomic potential energy surface (PES) models have been proposed that can potentially allow us to perform molecular dynamics simulations for large scale systems with ... 详细信息
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Visualizing structure and transitions in high-dimensional biological data (vol 54, pg 781, 2019)
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NATURE BIOTECHNOLOGY 2020年 第1期38卷 108-108页
作者: Moon, Kevin R. van Dijk, David Wang, Zheng Gigante, Scott Burkhardt, Daniel B. Chen, William S. Yim, Kristina van den Elzen, Antonia Hirn, Matthew J. Coifman, Ronald R. Ivanova, Natalia B. Wolf, Guy Krishnaswamy, Smita Department of Mathematics and Statistics Utah State University Logan USA Cardiovascular Research Center section Cardiology Department of Internal Medicine Yale University New Haven USA Department of Computer Science Yale University New Haven USA School of Basic Medicine Qingdao University Qingdao China Yale Stem Cell Center Department of Genetics Yale University New Haven USA Computational Biology and Bioinformatics Program Yale University New Haven USA Department of Genetics Yale University New Haven USA Department of Computational Mathematics Science and Engineering Michigan State University East Lansing USA Department of Mathematics Michigan State University East Lansing USA Applied Mathematics Program Yale University New Haven USA Department of Genetics Center for Molecular Medicine University of Georgia Athens USA Department of Mathematics and Statistics Université de Montréal Montréal Canada Mila—Quebec Artificial Intelligence Institute Montréal Canada
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
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Deep density: Circumventing the kohn-sham equations via symmetry preserving neural networks
arXiv
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arXiv 2019年
作者: Zepeda-Núñez, Leonardo Chen, Yixiao Zhang, Jiefu Zhang, Linfeng Jia, Weile Lin, Lin Department of Mathematics University of Wisconsin-Madison MadisonWI53706 United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Department of Mathematics University of California Berkeley BerkeleyCA94720 United States Department of Mathematics University of California Berkeley Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States
The recently developed Deep Potential [Phys. Rev. Lett. 120, 143001, 2018] is a powerful method to represent general inter-atomic potentials using deep neural networks. The success of Deep Potential rests on the prope... 详细信息
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Improving Bayesian local spatial models in large data sets
arXiv
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arXiv 2019年
作者: Lenzi, Amanda Castruccio, Stefano Rue, Håvard Genton, Marc G. Statistics Program King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Applied and Computational Mathematics and Statistics University of Notre Dame Notre DameIN46556 United States
Environmental processes resolved at a sufficiently small scale in space and time will inevitably display non-stationary behavior. Such processes are both challenging to model and computationally expensive when the dat... 详细信息
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