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检索条件"机构=Program in Applied and Computational Mathematics"
1033 条 记 录,以下是491-500 订阅
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Inverting the markovian projection, with an application to local stochastic volatility models
arXiv
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arXiv 2019年
作者: Lacker, Daniel Shkolnikov, Mykhaylo Zhang, Jiacheng Ieor Department Columbia University New YorkNY10027 United States Orfe Department Bendheim Center for Finance Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 Orfe Department Princeton University PrincetonNJ08544 United States
We study two-dimensional stochastic differential equations (SDEs) of McKean- Vlasov type in which the conditional distribution of the second component of the solution given the first enters the equation for the first ... 详细信息
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Characterization of maximally random jammed sphere packings. III. Transport and electromagnetic properties via correlation functions
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Physical Review E 2018年 第1期97卷 012118-012118页
作者: Michael A. Klatt Salvatore Torquato Department of Chemistry Department of Physics Princeton Institute for the Science and Technology of Materials and Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA
In the first two papers of this series, we characterized the structure of maximally random jammed (MRJ) sphere packings across length scales by computing a variety of different correlation functions, spectral function... 详细信息
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Precise algorithms to compute surface correlation functions of two-phase heterogeneous media and their applications
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Physical Review E 2018年 第1期98卷 013307-013307页
作者: Zheng Ma Salvatore Torquato Department of Chemistry Department of Physics Princeton Institute for the Science and Technology of Materials and Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA
The quantitative characterization of the microstructure of random heterogeneous media in d-dimensional Euclidean space Rd via a variety of n-point correlation functions is of great importance, since the respective inf... 详细信息
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Multiscale PHATE Exploration of SARS-CoV-2 Data Reveals Multimodal Signatures of Disease
Research Square
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Research Square 2021年
作者: Kuchroo, Manik Huang, Jessie Wong, Patrick Grenier, Jean-Christophe Shung, Dennis Tong, Alexander Lucas, Carolina Klein, Jon Burkhardt, Daniel B. Gigante, Scott Godavarthi, Abhinav Rieck, Bastian Israelow, Benjamin Simonov, Michael Mao, Tianyang Oh, Ji Eun Silva, Julio Takahashi, Takehiro Odio, Camila D. Casanovas-Massana, Arnau Fournier, John Farhadian, Shelli Dela Cruz, Charles S. Ko, Albert I. Hirn, Matthew J. Wilson, F. Perry Hussin, Julie Wolf, Guy Iwasaki, Akiko Krishnaswamy, Smita Department of Neuroscience Yale University New HavenCT United States Department of Computer Science Yale University New HavenCT United States Department of Immunobiology Yale University New HavenCT United States Montreal Heart Institute MontréalQC Canada Department of Medicine Yale University New HavenCT United States Department of Genetics Yale University New HavenCT United States Computational Biology Bioinformatics Program Yale University New HavenCT United States Department of Applied Mathematics Yale University New HavenCT United States Department of Biosystems Science and Engineering ETH Zurich Switzerland Department of Epidemiology of Microbial Diseases Yale School of Public Health New HavenCT United States Department of Medicine Section of Infectious Diseases Yale University School of Medicine New HavenCT United States Department of Medicine Section of Pulmonary and Critical Care Medicine Yale University School of Medicine New HavenCT United States Department of Computational Mathematics Science and Engineering Michigan State University East LansingMI United States Department of Mathematics Michigan State University East LansingMI United States Clinical and Translational Research Accelerator Department of Medicine Yale University New HavenCT United States Faculty of Medicine Université de Montréal Québec Canada Mila – Quebec AI institute MontréalQC Canada Department of Mathematics and Statistics Université de Montréal MontréalQC Canada Howard Hughes Medical Institute Chevy ChaseMD United States
The biomedical community is producing increasingly high dimensional datasets, integrated from hundreds of patient samples, which current computational techniques struggle to explore. To uncover biological meaning from... 详细信息
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Active learning of uniformly accurate inter-atomic potentials for materials simulation
arXiv
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arXiv 2018年
作者: Zhang, Linfeng Lin, De-Ye Wang, Han Car, Roberto Weinan, E. Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Institute of Applied Physics and Computational Mathematics Huayuan Road 6 Beijing100088 China CAEP Software Center for High Performance Numerical Simulation Huayuan Road 6 Beijing100088 China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Huayuan Road 6 Beijing100088 China Department of Chemistry Department of Physics Program in Applied and Computational Mathematics Princeton Institute for the Science and Technology of Materials Princeton University PrincetonNJ08544 United States Department of Mathematics and Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Beijing Institute of Big Data Research Beijing100871 China
An active learning procedure called Deep Potential Generator (DP-GEN) is proposed for the construction of accurate and transferable machine learning-based models of the potential energy surface (PES) for the molecular... 详细信息
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Deterministic guarantees for Burer–Monteiro factorizations of smooth semidefinite programs
arXiv
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arXiv 2018年
作者: Boumal, Nicolas Voroninski, Vladislav Bandeira, Afonso S. Mathematics Department Program in Applied and Computational Mathematics Princeton University Department of Mathematics Center for Data Science Courant Institute of Mathematical Sciences New York University
We consider semidefinite programs (SDPs) with equality constraints. The variable to be optimized is a positive semidefinite matrix X of size n. Following the Burer–Monteiro approach, we optimize a factor Y of size n&... 详细信息
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Adaptive coupling of a deep neural network potential to a classical force field
arXiv
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arXiv 2018年
作者: Zhang, Linfeng Wang, Han Weinan, E. Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Huayuan Road 6 Beijing100088 China Department of Mathematics and Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Center for Data Science and Beijing International Center for Mathematical Research Peking University China Beijing Institute of Big Data Research Beijing100871 China
An adaptive modeling method (AMM) that couples a deep neural network potential and a classical force field is introduced to address the accuracy-efficiency dilemma faced by the molecular simulation community. The AMM ... 详细信息
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DeePCG: Constructing coarse-grained models via deep neural networks
arXiv
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arXiv 2018年
作者: Weinan, E. Zhang, Linfeng Han, Jiequn Wang, Han Car, Roberto Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Institute of Applied Physics and Computational Mathematics Fenghao East Road 2 Beijing100094 China Caep Software Center for High Performance Numerical Simulation Huayuan Road 6 Beijing100088 China Program in Applied and Computational Mathematics Princeton Institute for Science and Technology of Materials Princeton University PrincetonNJ08544 United States Department of Mathematics and Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Beijing Institute of Big Data Research Beijing100871 China
We introduce a general framework for constructing coarse-grained potential models without ad hoc approximations such as limiting the potential to two- A nd/or three-body contributions. The scheme, called Deep Coarse-G... 详细信息
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How SGD selects the global minima in over-parameterized learning: a dynamical stability perspective  18
How SGD selects the global minima in over-parameterized lear...
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Proceedings of the 32nd International Conference on Neural Information Processing Systems
作者: Lei Wu Chao Ma Weinan E. School of Mathematical Sciences Peking University Beijing P.R. China Program in Applied and Computational Mathematics Princeton University Princeton NJ Department of Mathematics and Program in Applied and Computational Mathematics Princeton University Princeton NJ and Beijing Institute of Big Data Research Beijing P.R. China
The question of which global minima are accessible by a stochastic gradient decent (SGD) algorithm with specific learning rate and batch size is studied from the perspective of dynamical stability. The concept of non-...
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The Nystrom Extension for Signals Defined on a Graph
The Nystrom Extension for Signals Defined on a Graph
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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
作者: Ayelet Heimowitz Yonina C. Eldar The Program in Applied and Computational Mathematics Princeton University Princeton NJ USA Department of Electrical Engineering Technion Haifa Israel
In this paper we introduce a computationally efficient solution to the problem of graph signal interpolation. Our solution is derived using the Nyström extension and is due to the properties of the Markov matrix ... 详细信息
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