Machine-Learning-Based Modeling of Multiscale Dynamic Systems with Non-Markovian State-Dependent Memory

具有非马尔可夫状态相关记忆的多尺度动态系统的基于机器学习的建模

• 批准号: 2110981
• 负责人: Huan Lei
• 金额: $ 21万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2110981&HistoricalAwards=false
• 关键词: Machine; Learning; Based; Modeling; Multiscale

Accurate modeling of multiscale dynamic systems has been a long-standing problem that has attracted tremendous interest in computational mathematics and applications in fluid physics, chemical engineering, and materials science. The focus of this project is on multiscale systems that do not have clear scale separation, such as nanoscale multi-physics systems. For such systems the existing approaches feature so-called memory effects and non-Markovian behavior, which limit their understanding and control. The project will develop new advanced computational tools based on machine learning to construct highly accurate models of multiscale systems directly from first-principle-based descriptions. The constructed models retain a molecular-level fidelity and can be broadly applied to investigate the dynamic processes relevant to material design, drug delivery, and soft matter assembly. This project will also provide interdisciplinary training and research experiences for both graduate and undergraduate students. The research in this project will address a fundamental problem in model reduction and multiscale modeling for dynamic systems without clear scale separation. Current empirical models generally show limitations to retain the microscale level fidelity due to the over-simplification of the state-dependent Markovian memory term arising from the unresolved dynamics on microscale. This gap can be bridged by the methods based on machine-learning developed in this project; these will provide a general framework to learn a set of non-Markovian features from the full descriptions, and simultaneously, train the stochastic reduced model by embedding the state-dependent memory term in the extended dynamics of the non-Markovian features. Different from the conventional machine learning approaches for modeling dynamic equations, the models developed in this project are based on rigorous projection formalism and retain a clear physical interpretation. Consistent noise terms can be naturally introduced to the reduced models, which are well-suited for studying complex dynamic systems out of equilibrium. As a result, the methods can be employed to investigate open scientific questions such as the nanoscale assembly process by faithfully accounting for the microscale interactions. In the long term, this project will provide more comprehensive computational tools for establishing predictive modeling and control of such systems.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.

多尺度动态系统的精确建模一直是计算数学领域的一个长期问题，在流体物理、化学工程和材料科学等领域的应用中引起了极大的兴趣。该项目的重点是没有明确尺度分离的多尺度系统，如纳米级多物理系统。对于这样的系统，现有的方法具有所谓的记忆效应和非马尔可夫行为，这限制了他们的理解和控制。该项目将开发基于机器学习的新的先进计算工具，以直接从基于第一原理的描述中构建多尺度系统的高精度模型。 所构建的模型保持了分子水平的保真度，并可广泛应用于研究相关的材料设计，药物输送，和软物质组装的动态过程。 该项目还将为研究生和本科生提供跨学科的培训和研究经验。本计画的研究将针对无明确尺度分离的动态系统，解决模型降阶与多尺度建模的基本问题。目前的经验模型一般表现出局限性，以保持微尺度水平的保真度，由于过度简化的状态依赖马尔可夫记忆项所产生的未解决的动力学微尺度。这一差距可以通过该项目中开发的基于机器学习的方法来弥合;这些方法将提供一个通用框架，从完整的描述中学习一组非马尔可夫特征，同时通过将状态依赖记忆项嵌入非马尔可夫特征的扩展动态中来训练随机简化模型。与传统的机器学习方法建模动力学方程不同，该项目中开发的模型基于严格的投影形式主义，并保留了清晰的物理解释。一致的噪声项可以自然地引入到简化模型中，这非常适合于研究复杂的动态系统的平衡。因此，该方法可以用来调查开放的科学问题，如纳米组装过程中忠实地占微尺度的相互作用。从长远来看，这个项目将提供更全面的计算工具，建立预测建模和控制这样的system.This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

Construction of Coarse-Grained Molecular Dynamics with Many-Body Non-Markovian Memory.

利用多体非马尔可夫记忆构建粗粒度分子动力学。

• DOI: 10.1103/physrevlett.131.177301
• 发表时间: 2023
• 期刊: Physical review letters
• 影响因子: 8.6
• 作者: Liyao Lyu;H. Lei
• 通讯作者: H. Lei

Machine learning assisted coarse-grained molecular dynamics modeling of meso-scale interfacial fluids

机器学习辅助介观尺度界面流体的粗粒度分子动力学建模

• DOI: 10.1063/5.0131567
• 发表时间: 2023
• 期刊: The Journal of Chemical Physics
• 作者: Ge, Pei;Zhang, Linfeng;Lei, Huan
• 通讯作者: Lei, Huan

Data-driven construction of stochastic reduced dynamics encoded with non-Markovian features

用非马尔可夫特征编码的随机简化动态的数据驱动构造

• DOI: 10.1063/5.0130033
• 发表时间: 2023
• 期刊: The Journal of Chemical Physics
• 作者: She, Zhiyuan;Ge, Pei;Lei, Huan
• 通讯作者: Lei, Huan