CAREER: Integrating Information Theory with Data-Driven Mechanics: Toward Predictive Modeling of Material Behavior far from Equilibrium

职业：将信息理论与数据驱动力学相结合：对远离平衡的材料行为进行预测建模

• 批准号: 2047506
• 负责人: Celia Reina
• 金额: $ 65.2万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2047506&HistoricalAwards=false
• 关键词: CAREER; Integrating; Information; Theory; Data

This Faculty Early Career Development (CAREER) award will support fundamental research on the modeling of material behavior far from equilibrium, such as crystal plasticity in metallic systems, solid-solid phase transformations, or particle rearrangements in disordered media. In all these examples, the observable macroscale material behavior is affected by the underlying atomic or particle microstructure. The lack of understanding of the linkage between microstructure and macroscale mechanical response hinders high-fidelity predictive capacity critical to structural and industrial applications, e.g., in infrastructure, transportation, and manufacturing. To illustrate further, in manufacturing, it leads to economic losses and barriers to innovation in processing of particles, forming of metals, and additive manufacturing. Progress aimed at closing this gap is therefore important and needed to advance the national health, prosperity, and welfare, as well as to secure the national defense. In addition, this grant will support an educational and outreach program to increase gender and racial equity in STEM fields, as well as to promote critical thinking and scientific literacy within the public.The long-term goal of this project is to realize a computational paradigm that delivers continuum macroscopic models of far-from-equilibrium mechanical behavior with atomic or particle fidelity. In pursuit of this goal, the objective is twofold: 1) integrate information theory practices with machine learning to identify precise macroscopic state variables that would represent coarse-grained multiscale processes starting from the atomic or discrete particle behavior, and 2) use state-of-the-art physics-based variational formulation to determine the continuum evolution equations for these variables. The approach will be validated using experimental rheological data for shearing of dense packings of colloidal particles, a scientifically rich and technologically relevant example of far-from-equilibrium mechanical behavior.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.

该学院早期职业发展（CAREER）奖将支持远离平衡的材料行为建模的基础研究，例如金属系统中的晶体塑性，固-固相转化或无序介质中的颗粒重排。在所有这些示例中，可观察到的宏观尺度材料行为受到底层原子或颗粒微观结构的影响。缺乏对微观结构和宏观尺度力学响应之间联系的理解阻碍了对结构和工业应用至关重要的高保真预测能力，例如，基础设施、交通和制造业。进一步说明，在制造业中，它会导致经济损失，并阻碍颗粒加工、金属成型和增材制造的创新。因此，旨在缩小这一差距的进展是重要的，也是促进国家健康、繁荣和福利以及确保国防安全所必需的。此外，该基金还将支持一项教育和推广计划，以提高STEM领域的性别和种族平等，并促进公众的批判性思维和科学素养。该项目的长期目标是实现一种计算范式，提供原子或粒子保真度的远非平衡力学行为的连续宏观模型。为了实现这一目标，我们的目标有两个：1）将信息论实践与机器学习相结合，以识别精确的宏观状态变量，这些变量将代表从原子或离散粒子行为开始的粗粒度多尺度过程，以及2）使用最先进的基于物理学的变分公式来确定这些变量的连续演化方程。该方法将通过剪切胶体颗粒致密填料的实验流变学数据进行验证，这是一个科学丰富且技术相关的远离平衡机械行为的例子。该奖项反映了NSF的法定使命，并被认为值得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来支持。

项目成果

Emergence of viscosity and dissipation via stochastic bonds

• DOI: 10.1016/j.jmps.2021.104660
• 发表时间: 2021-10
• 期刊: Journal of the Mechanics and Physics of Solids
• 影响因子: 5.3
• 作者: Travis Leadbetter;A. Seiphoori;C. Reina;P. Purohit

Predicting the unobserved: A statistical mechanics framework for non-equilibrium material response with quantified uncertainty

预测未观察到的情况：具有量化不确定性的非平衡材料响应的统计力学框架

• DOI: 10.1016/j.jmps.2022.104779
• 发表时间: 2022
• 期刊: Journal of the Mechanics and Physics of Solids
• 影响因子: 5.3
• 作者: Huang, Shenglin;Graham, Ian R.;Riggleman, Robert A.;Arratia, Paulo E.;Fitzgerald, Steve;Reina, Celia
• 通讯作者: Reina, Celia

Second-order fast–slow dynamics of non-ergodic Hamiltonian systems: Thermodynamic interpretation and simulation

非遍历哈密顿系统的二阶快慢动力学：热力学解释和模拟

• DOI: 10.1016/j.physd.2021.133036
• 发表时间: 2021
• 期刊: Physica D: Nonlinear Phenomena
• 作者: Klar, Matthias;Matthies, Karsten;Reina, Celia;Zimmer, Johannes
• 通讯作者: Zimmer, Johannes

Variational Onsager Neural Networks (VONNs): A thermodynamics-based variational learning strategy for non-equilibrium PDEs

• DOI: 10.1016/j.jmps.2022.104856
• 发表时间: 2022-04-04
• 期刊: JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS
• 影响因子: 5.3
• 作者: Huang, Shenglin;He, Zequn;Reina, Celia
• 通讯作者: Reina, Celia