CAREER: New Mechanisms for Stability, Regularity and Long Time Dynamics of Partial Differential Equations
职业:偏微分方程稳定性、正则性和长期动力学的新机制
基本信息
- 批准号:1945179
- 负责人:
- 金额:$ 42.5万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2020
- 资助国家:美国
- 起止时间:2020-07-01 至 2025-06-30
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
The project focuses on mathematical analysis of nonlinear partial differential equations that are inspired by fluid dynamics and wave propagation. Understanding the dynamics of incompressible fluids, such as water and air at subsonic speed, is important for a variety of applications, ranging from the design of airplanes, boats and motors, to the study of oceans and the atmosphere. Coherent structures, such as vortices (eddies) and shear flows, are prominent features in fluid dynamics. The formation, stability, and evolution of coherent structures are critical fluid phenomena to understand in order to reduce drag, oscillation, and instability in scientific and engineering applications. The PI will develop new, innovative mathematical methods to analyze the dynamic properties of physically important coherent structures, which can resolve theoretical difficulties as well as provide powerful mathematical tools for practical applications. The PI will also study the interaction of radiation and particles in the context of wave maps, which have a deep connection to the classical field theories from mathematical physics. The proposed projects provide an ideal training ground for junior researchers in applying cutting edge mathematical analysis to study sophisticated physical phenomena in fluid dynamics and wave propagation. Graduate students will be actively involved in these research projects. The PI and collaborators aim to develop new methods that can effectively combine precise spectral and Fourier analysis in the context of nonlinear asymptotic stability problems of fluid dynamics. In many physical problems, the analysis of large coherent structures requires precise spectral analysis for the linearized flow, while Fourier analysis has proved indispensable in uncovering delicate nonlinear interactions. Thus, the techniques developed in the project may have a wider range of applications in other technically challenging perturbative problems. The PI will also study simpler models of fluid equations in an effort to understand the interaction and balance between vorticity stretching and vorticity transportation effects, which play a fundamental role in the regularity theory of three-dimensional Euler equations. For the wave maps equation, the main goal is to extend the "channel of energy" argument for outgoing waves to this technically challenging model to study the decoupling of radiation from solitons in a non-perturbative regime. These projects provide a wide range of problems for graduate students, who will learn to use tools from spectral analysis, Fourier analysis, dynamical systems, and numerical simulation, in the study of physically significant problems.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.
该项目侧重于受流体动力学和波传播启发的非线性偏微分方程的数学分析。了解不可压缩流体的动力学,如亚音速下的水和空气,对于各种应用都很重要,从飞机,船只和发动机的设计到海洋和大气的研究。相干结构,如旋涡(涡流)和剪切流,是流体动力学中的突出特征。相干结构的形成、稳定性和演化是理解的关键流体现象,以便在科学和工程应用中减少阻力、振荡和不稳定性。PI将开发新的、创新的数学方法来分析物理上重要的相干结构的动态特性,这可以解决理论困难,并为实际应用提供强大的数学工具。PI还将在波图的背景下研究辐射和粒子的相互作用,这与数学物理的经典场论有着深刻的联系。拟议的项目提供了一个理想的培训基地,初级研究人员在应用尖端的数学分析,研究复杂的物理现象,在流体动力学和波的传播。研究生将积极参与这些研究项目。 PI和合作者的目标是开发新的方法,可以有效地结合联合收割机精确的频谱和傅立叶分析的非线性渐近稳定性问题的流体动力学的背景下。在许多物理问题中,大型相干结构的分析需要对线性流进行精确的谱分析,而傅立叶分析在揭示微妙的非线性相互作用方面已被证明是不可或缺的。因此,在该项目中开发的技术可能有更广泛的应用在其他技术上具有挑战性的微扰问题。PI还将研究更简单的流体方程模型,以了解涡量拉伸和涡量传输效应之间的相互作用和平衡,这在三维欧拉方程的正则性理论中起着重要作用。对于波映射方程,主要目标是将传出波的“能量通道”参数扩展到这个技术上具有挑战性的模型,以研究在非微扰状态下辐射与孤子的解耦。这些项目为研究生提供了广泛的问题,他们将学习使用光谱分析,傅立叶分析,动力系统和数值模拟的工具,在物理上重要的问题的研究。这个奖项反映了NSF的法定使命,并已被认为是值得的支持,通过评估使用基金会的智力价值和更广泛的影响审查标准。
项目成果
期刊论文数量(4)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Nonlinear inviscid damping near monotonic shear flows
- DOI:10.4310/acta.2023.v230.n2.a2
- 发表时间:2020-01
- 期刊:
- 影响因子:3.7
- 作者:A. Ionescu;H. Jia
- 通讯作者:A. Ionescu;H. Jia
Linear Vortex Symmetrization: The Spectral Density Function
线性涡旋对称化:谱密度函数
- DOI:10.1007/s00205-022-01815-y
- 发表时间:2022
- 期刊:
- 影响因子:2.5
- 作者:Ionescu, Alexandru D.;Jia, Hao
- 通讯作者:Jia, Hao
On the Stability of Shear Flows in Bounded Channels, II: Non-monotonic Shear Flows
关于有界通道中剪切流的稳定性,II:非单调剪切流
- DOI:10.1007/s10013-023-00661-z
- 发表时间:2023
- 期刊:
- 影响因子:0.8
- 作者:Ionescu, Alexandru D.;Iyer, Sameer;Jia, Hao
- 通讯作者:Jia, Hao
Uniform Linear Inviscid Damping and Enhanced Dissipation Near Monotonic Shear Flows in High Reynolds Number Regime (I): The Whole Space Case
高雷诺数状态下的均匀线性无粘阻尼和增强耗散近单调剪切流 (I):整个空间案例
- DOI:10.1007/s00021-023-00794-8
- 发表时间:2023
- 期刊:
- 影响因子:1.3
- 作者:Jia, Hao
- 通讯作者:Jia, Hao
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Hao Jia其他文献
Predicting QoS of virtual machines via Bayesian network with XGboost-induced classes
使用 XGboost 诱导类通过贝叶斯网络预测虚拟机的 QoS
- DOI:
10.1007/s10586-020-03183-2 - 发表时间:
2020-09 - 期刊:
- 影响因子:4.4
- 作者:
Hao Jia;Yue Kun;Duan Liang;Zhang Binbin;Fu Xiaodong - 通讯作者:
Fu Xiaodong
WLnet: Towards an Approach for Robust Workload Estimation Based on Shallow Neural Networks
WLnet:一种基于浅层神经网络的鲁棒工作负载估计方法
- DOI:
10.1109/access.2020.3044732 - 发表时间:
2021 - 期刊:
- 影响因子:3.9
- 作者:
Zhe Sun;Binghua Li;Feng Duan;Hao Jia;Shan Wang;Yu Liu;Andrzej Cichocki;Cesar F. Caiafa;Jordi Solé-Casals - 通讯作者:
Jordi Solé-Casals
Microring modulator matrix integrated with mode multiplexer and de-multiplexer for on-chip optical interconnect
集成了模式复用器和解复用器的微环调制器矩阵,用于片上光学互连
- DOI:
10.1364/oe.25.000422 - 发表时间:
2016-08 - 期刊:
- 影响因子:3.8
- 作者:
Hao Jia;Lei Zhang;Jianfeng Ding;Lingchen Zheng;Chen Yuan;Lin Yang - 通讯作者:
Lin Yang
Magnetic-Field-Driven Reconfigurable Microsphere Arrays for Laser Display Pixels
用于激光显示像素的磁场驱动可重构微球阵列
- DOI:
10.1021/acsnano.2c08766 - 发表时间:
2023 - 期刊:
- 影响因子:17.1
- 作者:
Baipeng Yin;Hao Jia;Hong Wang;Rui Chen;Lixin Xu;Yong Sheng Zhao;Chuang Zhang;Jiannian Yao - 通讯作者:
Jiannian Yao
Nonreciprocal coherent coupling of nanomagnets by exchange spin waves
通过交换自旋波实现纳米磁体的非互易相干耦合
- DOI:
10.1007/s12274-020-3251-5 - 发表时间:
2020-05 - 期刊:
- 影响因子:9.9
- 作者:
Hanchen Wang;Jilei Chen;Tao Yu;Chuanpu Liu;Chenyang Guo;Song Liu;Ka Shen;Hao Jia;Tao Liu;Jianyu Zhang;Marco A. Cabero Z;Qiuming Song;Sa Tu;Mingzhong Wu;Xiufeng Han;Ke Xia;Dapeng Yu;Haiming Yu;Gerrit E. W. Bauer - 通讯作者:
Gerrit E. W. Bauer
Hao Jia的其他文献
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{{ truncateString('Hao Jia', 18)}}的其他基金
Conference: Recent advances in nonlinear Partial Differential Equations
会议:非线性偏微分方程的最新进展
- 批准号:
2346780 - 财政年份:2024
- 资助金额:
$ 42.5万 - 项目类别:
Standard Grant
FRG: Collaborative Research: Singularities in Incompressible Flows: Computer Assisted Proofs and Physics-Informed Neural Networks
FRG:协作研究:不可压缩流中的奇异性:计算机辅助证明和物理信息神经网络
- 批准号:
2245021 - 财政年份:2023
- 资助金额:
$ 42.5万 - 项目类别:
Standard Grant
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