Humans learn from the occurrence of events at different places and times to predict similar trajectories of events. We define loosely decoupled time (LDT) phenomena as two or more events that could occur in different ...
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Humans learn from the occurrence of events at different places and times to predict similar trajectories of events. We define loosely decoupled time (LDT) phenomena as two or more events that could occur in different places and across different timelines but share similarities in the nature of the event and the properties of the location. In this work, we improve the use of recurrent neural networks (RNN), particularly long short-term memory (LSTM) networks, to enable AI solutions that generate better time series predictions for LDT. We used similarity measures between the time series based on the time series properties detected by the LSTM and introduced embeddings representing these properties. The embeddings represent the properties of the event, which, coupled with the LSTM structure, can be clustered to identify similar temporally unaligned events. In this study, we explore methods of seeding a multivariate LSTM from time-invariant data related to the geophysical and demographic phenomena modeled by the LSTM. We applied these methods to time-series data derived from COVID-19 detected infection and death cases. We use publicly available socioeconomic data to seed the LSTM models, creating embeddings, to determine whether such seeding improves case predictions. The embeddings produced by these LSTMs are clustered to identify the best-matching candidates for forecasting evolving time series. Applying this method, we showed an improvement in the 10-day moving average predictions of disease propagation at the US County level.
The Lattice Boltzmann Method (LBM) is a class of Computational Fluid Dynamics methods which models the fluid as fictive particles. In this paper, we report our work on SunwayLB, which enables LBM based solutions aimin...
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The Lattice Boltzmann Method (LBM) is a class of Computational Fluid Dynamics methods which models the fluid as fictive particles. In this paper, we report our work on SunwayLB, which enables LBM based solutions aiming for industrial applications using advanced heterogeneous systems such as the Sunway supercomputers. We propose several techniques to boost the simulation speed and improve the scalability of SunwayLB, including a customized multi-level domain decomposition and data sharing scheme, a carefully orchestrated strategy to fuse kernels with different performance constraints for a more balanced workload, and optimization strategies for assembly code. Based on these optimization schemes, we manage to scale SunwayLB on three advanced supercomputers: Sunway TaihuLight, the new Sunway Supercomputer and a GPU cluster. On Sunway TaihuLight, our largest simulation involves up to 5.6 trillion lattice cells, achieving 11,245 billion cell updates per second (GLUPS), 77% memory bandwidth utilization and a sustained performance of 4.7 PFlops. We further improve the memory bandwidth utilization and computational efficiency using the unique features of a new generation of Sunway supercomputer. On the new Sunway Supercomputer, the largest simulation contains over 4.2 trillion lattice cells, resulting in 6,583 GLUPS, 81% memory bandwidth utilization and a sustained performance of 2.76 PFlops. To evaluate the portability of our code, we also adapt our code to a GPU cluster with tailored optimization techniques, resulting in 191x speedup and 83.8% memory bandwidth utilization. We demonstrate a series of computational experiments for extreme-large scale fluid flow, as examples of real-world applications, to check the validity and performance of our work. The results show that our implementation is competent to be a highly scalable and efficient solution for large-scale CFD problems on heterogeneous systems.
A new parallel normalized optimized approximate inverse algorithm, based on the concept of the "fish bone" computational approach satisfying an antidiagonal data dependency, for computing classes of explicit...
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A new parallel normalized optimized approximate inverse algorithm, based on the concept of the "fish bone" computational approach satisfying an antidiagonal data dependency, for computing classes of explicit approximate inverses, is introduced for symmetric multiprocessor systems. The parallel normalized explicit approximate inverses are used in conjunction with parallel normalized explicit preconditioned conjugate gradient square schemes, for the efficient solution of finite element sparse linear systems. The parallel design and implementation issues of the new proposed algorithms are discussed and the parallel performance is presented, using OpenMP. (c) 2007 Elsevier Inc. All rights reserved.
A method for trimming surfaces generated as solutions to partial differential equations (PDEs) is presented. The work we present here utilises the 2D parameter space on which the trim curves are defined whose projecti...
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A method for trimming surfaces generated as solutions to partial differential equations (PDEs) is presented. The work we present here utilises the 2D parameter space on which the trim curves are defined whose projection oil the parametrically represented PDE surface is then trimmed out. To do this we define the trim curves to be a set of boundary conditions which enable us to solve a low order elliptic PDE oil the parameter space. The chosen elliptic PDE is solved analytically, even in the case of a very general complex trim.. allowing the design process to be carried out interactively in real time. To demonstrate the capability for this technique we discuss a series of examples where trimmed PDE surfaces may be applicable. (c) 2006 Elsevier Ltd. All rights reserved.
To obtain the numerical value of the Uniform, Exponential and Pareto distributions is necessary to use numerical integration and its value is obtained by approximation and therefore affected by rounding or truncation ...
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ISBN:
(纸本)9781479930579
To obtain the numerical value of the Uniform, Exponential and Pareto distributions is necessary to use numerical integration and its value is obtained by approximation and therefore affected by rounding or truncation errors. Through the use of intervals, there is an automatic control error with reliable limits. The objective of the work is to analyze the computational complexity for computing the random variables with Uniform, Exponential and Pareto distributions in real and interval form in order to justify that, it to the use intervals to represent the real form of these variables, it is possible to control the propagation of errors and maintain the computational effort.
During the last ten years a number of interface specifications for the exchange of product model data between CAD systems as well as between CAD and other computer aided systems have been developed. Some of them are a...
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ISBN:
(纸本)0444814965
During the last ten years a number of interface specifications for the exchange of product model data between CAD systems as well as between CAD and other computer aided systems have been developed. Some of them are also standardized by national or international bodies. The capabilities of these interface specifications are different and cover various application areas, data formats and file format definitions. This article is focusing on freeform surfaces which are described and exchanged using standard interfaces. A general overview on different mathematical representations of freeform surfaces, the specification and application of freeform surfaces within standard interfaces, and a new methodology for the exact conversion between different freeform surface representations are presented.
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