Analysis of Partial Differential Equations with Cross-Diffusion and Stochastic Driving

具有交叉扩散和随机驱动的偏微分方程分析

基本信息

项目摘要

Second-order evolution partial differential equations (PDEs) involving cross-diffusion terms are frequently used in mathematical biology and present a challenge for mathematical analysis. An efficient technical tool to prove the existence of global-in-time solutions are entropy methods. In this context, a suitable functional provides global a-priori bounds so that existence can be obtained via a fixed-point argument. In addition, the functional can be used to show global decay to equilibrium in certain regimes. From the viewpoint of mathematical biology, cross-diffusion deterministic PDEs are a mean-field model and it is highly desirable to include stochastic effects due to finite-size effects or random external forcing. This naturally leads one to consider stochastically-forced partial differential equations (SPDEs). For cross-diffusion SPDEs, basically no mathematical analysis exists and one major goal of this project is to fill this substantial gap in our knowledge. From a technical point of view, SPDEs are challenging as one has to deal with very low-regularity noise terms in many cases. Here we propose a multi-faceted approach towards this problem. For local-in-time existence theory, we aim to study three different solution concepts for cross-diffusion SPDEs: mild solutions, weak solutions (in the probabilistic sense), and renormalized regularity-structure solutions. One key question will be to identify the barrier of noise regularity, where each different technique works for cross-diffusions SPDEs. Furthermore, we aim to contribute towards approximation methods for solutions by regularization (of the noise, of the diffusion, and via finite-dimensional approximations). For global-in-time-existence, we propose to transfer entropy method techniques from PDEs to the analysis of SPDEs. A main theme in this context will be the use of entropy variables in combination with global a-priori bounds. In summary, we aim to merge and significantly extend theories from several different mathematical areas within the framework of cross-diffusion PDEs.
涉及交叉扩散项的二阶进化偏微分方程(PDEs)是数学生物学中经常使用的方程,对数学分析提出了挑战。熵法是证明全局实时解存在性的有效技术工具。在这种情况下,合适的函数提供全局先验界,以便通过定点参数获得存在性。此外,该泛函还可用于显示在某些状态下的平衡的全局衰减。从数学生物学的角度来看,交叉扩散确定性偏微分方程是一个平均场模型,它非常希望包含有限尺寸效应或随机外部强迫引起的随机效应。这自然导致人们考虑随机强迫偏微分方程(SPDEs)。对于交叉扩散spde,基本上没有数学分析存在,这个项目的一个主要目标是填补我们知识上的这一实质性空白。从技术角度来看,spde具有挑战性,因为在许多情况下必须处理非常低的正则噪声项。在此,我们提出了解决这一问题的多方面方法。对于局部存在理论,我们研究了交叉扩散SPDEs的三种不同的解概念:温和解、弱解(在概率意义上)和重整正则化正则结构解。一个关键问题将是确定噪声规则的屏障,其中每种不同的技术都适用于交叉扩散spde。此外,我们的目标是通过正则化(噪声,扩散和有限维近似)为解的近似方法做出贡献。对于全局时间存在性,我们提出将熵法技术从偏微分方程转移到偏微分方程的分析。在这种情况下,一个主要的主题将是熵变量与全局先验边界的结合使用。总之,我们的目标是在交叉扩散偏微分方程的框架内合并和显著扩展来自几个不同数学领域的理论。

项目成果

期刊论文数量(6)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Global martingale solutions for quasilinear SPDEs via the boundedness-by-entropy method
通过熵有界法求拟线性 SPDE 的全局鞅解
Rigorous mean-field limit and cross-diffusion
Dynamics of Stochastic Reaction-Diffusion Equations
Pathwise mild solutions for quasilinear stochastic partial differential equations
  • DOI:
    10.1016/j.jde.2020.01.032
  • 发表时间:
    2018-02
  • 期刊:
  • 影响因子:
    2.4
  • 作者:
    C. Kuehn;A. Neamţu
  • 通讯作者:
    C. Kuehn;A. Neamţu
Global martingale solutions for a stochastic population cross-diffusion system
随机群体交叉扩散系统的全局鞅解
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Professor Christian Kühn, Ph.D.其他文献

Professor Christian Kühn, Ph.D.的其他文献

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{{ truncateString('Professor Christian Kühn, Ph.D.', 18)}}的其他基金

Geometric Desingularization of Higher Codimension Singularities in Fast-Slow Systems
快慢系统中高维奇点的几何去奇异化
  • 批准号:
    444753754
  • 财政年份:
  • 资助金额:
    --
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    Research Grants
Transport and Epidemic Networks: Graphs, Optimization and Simulation (TENGOS)
交通和流行病网络:图形、优化和模拟 (TNGOS)
  • 批准号:
    458548755
  • 财政年份:
  • 资助金额:
    --
  • 项目类别:
    Research Grants
Stochastic Epidemic-Economic Adaptive Network Dynamics
随机流行病-经济自适应网络动力学
  • 批准号:
    496237661
  • 财政年份:
  • 资助金额:
    --
  • 项目类别:
    Research Grants
Quasi-Steady State Approximation for Partial Differential Equations
偏微分方程的准稳态近似
  • 批准号:
    456754695
  • 财政年份:
  • 资助金额:
    --
  • 项目类别:
    Research Grants

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Conference: Geometric Measure Theory, Harmonic Analysis, and Partial Differential Equations: Recent Advances
会议:几何测度理论、调和分析和偏微分方程:最新进展
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    2402028
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    2024
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Theoretical Guarantees of Machine Learning Methods for High Dimensional Partial Differential Equations: Numerical Analysis and Uncertainty Quantification
高维偏微分方程机器学习方法的理论保证:数值分析和不确定性量化
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A new numerical analysis for partial differential equations with noise
带有噪声的偏微分方程的新数值分析
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International Conference on Harmonic Analysis, Partial Differential Equations, and Geometric Measure Theory
调和分析、偏微分方程和几何测度理论国际会议
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    2247067
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Toward a global analysis on solutions of nonlinear partial differential equations
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