Methodology for Multi Time-Scale Nonlinear Dynamical Spatio-Temporal Statistical Models
多时间尺度非线性动态时空统计模型方法
基本信息
- 批准号:1811745
- 负责人:
- 金额:$ 22.5万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2018
- 资助国家:美国
- 起止时间:2018-08-01 至 2021-07-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Scientists and engineers are increasingly aware of the importance of accurately characterizing various sources of uncertainty when trying to understand complex systems such as those that vary across time and space. Examples of such systems include how ocean heating influences convective clouds in the tropics, which in turn, can influence severe weather and habitat conditions over North America; or, how a migratory species interacts with its environment and competitive pressures from both predators and prey. When performing statistical modeling on such complex spatio-temporal phenomena, the scientific goal is typically either inference, prediction, or forecasting, all of which require some measure of uncertainty. To accomplish these goals through modeling, one must synthesize information from a variety of sources, including direct observations, indirect (remotely sensed) observations, surrogate observations (mechanistic model output), previous empirical results, expert opinion, and scientific knowledge. This information must then integrate into a process model that can represent the complexity of the interacting processes, and account for uncertainty. This research is concerned with building these models in a way that can account for complex interactions across different time scales.This project concerns the development of a methodological framework for parsimonious and computationally efficient models for multi time-scale nonlinear dynamical spatio-temporal processes that accounts for the interaction across processes and time scales in such a way as to accommodate uncertainty in data, processes, and parameters. In particular, the project will focus on a hybrid model that combines elements of a generalized quadratic nonlinear spatio-temporal dynamical model with a recurrent neural network model. However, this methodology will focus on models for processes that involve multiple time scales of variability. This will include the development of computationally efficient algorithms that can deal with the extreme curse of dimensionality in the state and parameter spaces associated with complex interacting nonlinear phenomena by adapting, extending and combining approaches from both statistics and machine learning. Not only will the proposed modeling and computational methodology be an advancement in statistics, but it will be useful across a broad range of disciplines that deal with complex multi time-scale dynamical processes such as brain science, climatology, demography, econometrics, fisheries, ecology, meteorology, oceanography, and wildlife biology. In addition, the project will contribute to STEM education through training a graduate research assistant, who will gain inter-disciplinary experience. In addition, the project will foster undergraduate interest in the STEM disciplines by employing undergraduate research assistants to help with the development of visualization tools for spatio-temporal data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
科学家和工程师们越来越意识到,在试图理解诸如跨越时间和空间变化的复杂系统时,准确地描述各种不确定性来源的重要性。这类系统的例子包括:海洋加热如何影响热带地区的对流云,而对流云反过来又会影响北美的恶劣天气和栖息地条件;或者,迁徙物种如何与环境以及来自捕食者和猎物的竞争压力相互作用。当对这种复杂的时空现象进行统计建模时,科学目标通常是推断、预测或预测,所有这些都需要一定程度的不确定性。为了通过建模实现这些目标,必须综合各种来源的信息,包括直接观测、间接(遥感)观测、替代观测(机械模型输出)、以前的经验结果、专家意见和科学知识。然后,这些信息必须集成到一个过程模型中,该模型可以表示交互过程的复杂性,并考虑不确定性。这项研究关注的是以一种可以解释不同时间尺度上复杂相互作用的方式建立这些模型。该项目涉及为多时间尺度非线性动态时空过程的简约且计算效率高的模型开发方法框架,该模型考虑了过程和时间尺度之间的相互作用,以适应数据、过程和参数中的不确定性。特别是,该项目将重点放在将广义二次非线性时空动力学模型与递归神经网络模型相结合的混合模型上。然而,这种方法将侧重于涉及多个变异性时间尺度的过程的模型。这将包括开发计算效率高的算法,通过适应、扩展和结合统计学和机器学习的方法,这些算法可以处理与复杂相互作用的非线性现象相关的状态和参数空间中的极端维数诅咒。所提出的建模和计算方法不仅是统计学的进步,而且在处理复杂的多时间尺度动态过程的广泛学科中也很有用,如脑科学、气候学、人口学、计量经济学、渔业、生态学、气象学、海洋学和野生动物生物学。此外,该项目将通过培养研究生研究助理来促进STEM教育,他们将获得跨学科的经验。此外,该项目将通过雇用本科生研究助理帮助开发时空数据可视化工具,培养本科生对STEM学科的兴趣。该奖项反映了美国国家科学基金会的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。
项目成果
期刊论文数量(11)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
A Bayesian Markov Model with Pólya-Gamma Sampling for Estimating Individual Behavior Transition Probabilities from Accelerometer Classifications
采用 Pólya-Gamma 采样的贝叶斯马尔可夫模型,用于根据加速度计分类估计个体行为转移概率
- DOI:10.1007/s13253-020-00399-y
- 发表时间:2020
- 期刊:
- 影响因子:0
- 作者:Schafer, Toryn L.;Wikle, Christopher K.;VonBank, Jay A.;Ballard, Bart M.;Weegman, Mitch D.
- 通讯作者:Weegman, Mitch D.
Measuring, mapping, and uncertainty quantification in the space-time cube
时空立方体中的测量、绘图和不确定性量化
- DOI:10.1007/s13163-020-00359-7
- 发表时间:2020
- 期刊:
- 影响因子:0
- 作者:Cressie, Noel;Wikle, Christopher K.
- 通讯作者:Wikle, Christopher K.
Alternative Learning Strategies for Collective Animal Movement
集体动物运动的替代学习策略
- DOI:
- 发表时间:2019
- 期刊:
- 影响因子:0
- 作者:Schafer, T.L.J.;Wikle, C.K.
- 通讯作者:Wikle, C.K.
Bayesian inverse reinforcement learning for collective animal movement
集体动物运动的贝叶斯逆强化学习
- DOI:
- 发表时间:2021
- 期刊:
- 影响因子:0
- 作者:Schafer, T.L.J.;Wikle, C.K.;Hooten, M.B.
- 通讯作者:Hooten, M.B.
Comparison of Deep Neural Networks and Deep Hierarchical Models for Spatio-Temporal Data
- DOI:10.1007/s13253-019-00361-7
- 发表时间:2019-02
- 期刊:
- 影响因子:0
- 作者:C. Wikle
- 通讯作者:C. Wikle
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Christopher Wikle其他文献
Christopher Wikle的其他文献
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{{ truncateString('Christopher Wikle', 18)}}的其他基金
University of Missouri Black Migrations Symposium
密苏里大学黑人移民研讨会
- 批准号:
1906109 - 财政年份:2019
- 资助金额:
$ 22.5万 - 项目类别:
Standard Grant
Type 1: Collaborative Research: Bayesian Hierarchical Climate Prediction LO2170174
类型 1:合作研究:贝叶斯分级气候预测 LO2170174
- 批准号:
1049093 - 财政年份:2011
- 资助金额:
$ 22.5万 - 项目类别:
Standard Grant
Collaborative Research: Estimating Ecosystem Model Uncertainties in Pan-Regional Syntheses and Climate Change Impacts on Coastal Domains of the North Pacific Ocean
合作研究:估计泛区域综合中的生态系统模型不确定性和气候变化对北太平洋沿海地区的影响
- 批准号:
0814934 - 财政年份:2008
- 资助金额:
$ 22.5万 - 项目类别:
Standard Grant
MSPA-CSE: Statistical Methods for Precipitation Nowcasting and Verification
MSPA-CSE:降水临近预报和验证的统计方法
- 批准号:
0434213 - 财政年份:2004
- 资助金额:
$ 22.5万 - 项目类别:
Standard Grant
Collaborative Research: CMG: Ocean Circulation Climatology and Dynamics Using Bayesian Hierarchical Methods
合作研究:CMG:使用贝叶斯分层方法的海洋环流气候学和动力学
- 批准号:
0222057 - 财政年份:2002
- 资助金额:
$ 22.5万 - 项目类别:
Standard Grant
Collaborative Proposal: FRG: Statistical Analysis of Uncertainty in Climate Change
合作提案:FRG:气候变化不确定性的统计分析
- 批准号:
0139903 - 财政年份:2002
- 资助金额:
$ 22.5万 - 项目类别:
Standard Grant
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