Solution of Inverse Problems with Adaptive Models
Solution of Inverse Problems with Adaptive Models
批准号:
0635194
负责人:
Adrian Sandu
金额:
$18.61万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2006
资助国家:
美国
项目状态:
已结题
起止时间:
2006-09-15 至 2010-08-31
中文摘要
偏微分方程(PDE)控制的大系统的参数估计、数据同化和最优控制等反问题在大气科学和海洋学、最优流量控制和国土安全等许多领域都具有重要意义。并调整计算模式以控制数值误差和保持解的定性特征。相比之下,迄今为止,大多数逆问题都是使用非自适应方法解决的,因为获得自适应模拟的梯度具有相当大的挑战。该项目的目标是推进在自适应模型的背景下求解逆问题所需的基本算法。本文的研究成果主要体现在以下几个方面:(1)理解自适应网格方法的离散伴随问题 细化、自适应时间步进和自适应计算模式 (e.g.,(2)开发技术以最大限度地减少 离散伴随格式和(连续)伴随偏微分方程;(3)发展先验伴随误差估计;(4)评估不同伴随方法对性能的影响, 几种数值优化方案;(5)将新算法应用于真实的资料同化问题, 自适应正演模型求解反问题的一般算法和方法有望对包括大气科学、海洋学、环境科学、流的最优控制、结构力学、国土安全等在内的许多领域产生广泛的影响。
英文摘要
Inverse problems like parameter estimation, data assimilation, and optimalcontrol for large scale systems governed by partial differential equations(PDEs) are of considerable importance in many fields including atmosphericscience and oceanography, optimal flow control, and homeland security.State-of-the-art solvers for large scale PDEs adaptively refine the timestep and the mesh, and adjust the computational pattern in order tocontrol the numerical errors and to preserve the qualitative features ofthe solution. In contrast, most inverse problems to date have been solvedusing non-adaptive methods due to the considerable challenges associated with obtaining gradients for adaptive simulations. The goal of the project is to advance the fundamental algorithms neededfor solving inverse problems in the context of adaptive models. Theintellectual merit of this work is substantiated by the following researchelements: (1) understand the discrete adjoints for methods that use adaptive mesh refinement, adaptive time stepping, and adaptive computational patterns (e.g., upwinding or flux limiting);(2) develop techniques to minimize the inconsistencies between the discrete adjoint scheme and the (continuous) adjoint PDE;(3) develop apriori adjoint error estimates;(4) assess the impact of different adjoint approaches on the performance of several numerical optimization schemes; and(5) apply the new algorithms to real data assimilation problems in oceanography.The general algorithms and methodologies for solving inverse problems withadaptive forward models are expected to have a broad impact on many fieldsincluding atmospheric sciences, oceanography, environmental sciences,optimal control of flows, structural mechanics, homeland security, etc.
期刊论文(0)
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科研奖励(0)
会议论文
Transforming Reduced-Order Models of Fluids with Data Assimilation
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批准号:1953113
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项目类别:Standard Grant
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资助金额:$20.0万
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财政年份:2020
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负责人:Adrian Sandu
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依托单位:
CDS&E: Space-Time Parallel Algorithms for Solving PDE-Constrained Optimization Problems
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批准号:1709727
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资助金额:$50.0万
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财政年份:2017
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负责人:Adrian Sandu
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依托单位:
AF: Small: General Linear Multimethods for the Time Integration of Multiscale Multiphysics Problems
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批准号:1613905
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项目类别:Standard Grant
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资助金额:$50.0万
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财政年份:2016
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负责人:Adrian Sandu
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依托单位:
Collaborative Research: Construction, Analysis, Implementation and Application of New Efficient Exponential Integrators
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批准号:1419003
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项目类别:Standard Grant
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资助金额:$25.0万
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财政年份:2014
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负责人:Adrian Sandu
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依托单位:
A Fully Discrete Framework for the Adaptive Solution of Inverse Problems
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批准号:1218454
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资助金额:$25.0万
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财政年份:2012
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负责人:Adrian Sandu
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依托单位:
Collaborative Research: A multiscale unified simulation environment for geoscientific applications
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批准号:0904397
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项目类别:Standard Grant
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资助金额:$23.9万
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财政年份:2009
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负责人:Adrian Sandu
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依托单位:
Collaborative Research: A Computational Framework for Assessing the Observation Impact in Air Quality Forecasting
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批准号:0915047
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项目类别:Standard Grant
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资助金额:$41.86万
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财政年份:2009
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负责人:Adrian Sandu
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依托单位:
CIF:Small: General Linear Time-stepping Methods for Large-Scale Simulations
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批准号:0916493
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项目类别:Standard Grant
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资助金额:$31.23万
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财政年份:2009
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负责人:Adrian Sandu
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依托单位:
Multirate Time Integration Algorithms for Adaptive Simulations of PDEs
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批准号:0515170
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项目类别:Continuing Grant
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资助金额:$18.0万
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财政年份:2005
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负责人:Adrian Sandu
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依托单位:
CAREER: Development of Computational Methods for the New Generation of Air Quality Models
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批准号:0413872
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项目类别:Continuing Grant
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资助金额:$0.0万
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财政年份:2003
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负责人:Adrian Sandu
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依托单位:
CAREER: Development of Computational Methods for the New Generation of Air Quality Models
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批准号:0093139
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项目类别:Continuing Grant
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资助金额:$32.57万
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财政年份:2001
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负责人:Adrian Sandu
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依托单位:
国内基金
海外基金
新型简化Inverse Lax-Wendroff方法的发展与应用
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批准号:--
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项目类别:青年科学基金项目
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资助金额:30万元
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批准年份:2022
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负责人:程自强
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依托单位:
基于高阶格式的Inverse Lax-Wendroff方法及其稳定性分析
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批准号:11801143
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项目类别:青年科学基金项目
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资助金额:25.0万元
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批准年份:2018
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负责人:李婷婷
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依托单位: