CAREER: Computational Failure Mechanics Across Multiple Scales with Deep Reinforcement Learning

职业：具有深度强化学习的跨多个尺度的计算故障机制

• 批准号: 1846875
• 负责人: WaiChing Sun
• 金额: $ 50万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-02-15 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1846875&HistoricalAwards=false
• 关键词: CAREER; Computational; Failure; Mechanics; Across

This Faculty Early Career Development (CAREER) Program grant will support fundamental research in using human-interpretable knowledge generated by artificial/augmented intelligence (AI) to discover hidden physics mechanisms that lead to the failure of materials. While there has been considerable progress made in basic understanding of various failure mechanisms, e.g. brittle fracture, strain localization, and ductile flow, the recent advancements in experimental techniques, such as digital image correlation and micro-computed tomography imaging, have led to an influx of data on failure that is not always easy to incorporate into models manually. The current effort leverages the AI's ability to repeatedly generate and test hypotheses such that it can self-identify and discover mistakes in previous modeling efforts and identify new physics too subtle or difficult to identify and interpret manually. These computer-generated discoveries will be a powerful tool in accelerating the progress of science. By understanding how failures of materials and structures occur, the research will advance national prosperity and welfare by helping engineers making more efficient, robust and precise designs-by-analyses for infrastructure, structural components and devices. As part of the grant, the PI will also facilitate mentoring relationships with selected underrepresented high school students and high school teachers in the Harlem district of New York City to enable future generations of engineers and scientists in leveraging the opportunities afforded by AI. A new meta-modeling paradigm is planned to adaptively generate models to capture the effects of evolving microstructures due to micro-cracks, plastic slip, and wear at sub-scales, and then to recursively upscale responses to the scale of interest. By conceptualizing a constitutive law (theoretical or data-driven) as a directed multi-graph, i.e. a flow network of information, we idealize the process of writing a constitutive law as a combination of actions operated on the directed multi-graph. With a reward function defined as a function of accuracy, robustness, speed, and consistency, we then invent a game whose goal is to maximize the rewards with finite actions (e.g. shape of yield surface, hardening rules, damage, nonlocality, refinement criterion) against constraints (e.g. material-frame indifference, thermodynamic laws). This technique is then applied in hierarchical and concurrent multiscale coupling models. In the hierarchical models, hybridized machine learning models can be used as a surrogate to bridge scales. In the concurrent model, a multi-phase field model is used to enable several types of models (theoretical, data-driven, hybridized, homogenization-from sub-scale-simulations) to be deployed at different domains of interests (e.g. crack tip, shear band) to yield the most accurate simulations.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）计划拨款将支持基础研究，利用人工/增强智能（AI）产生的人类可解释的知识来发现导致材料失效的隐藏物理机制。虽然已经取得了相当大的进展，在基本了解各种故障机制，如脆性断裂，应变局部化，和韧性流动，最近的进步，在实验技术，如数字图像相关性和微计算机断层扫描成像，导致了大量的数据涌入故障，并不总是容易纳入模型手动。目前的努力利用人工智能重复生成和测试假设的能力，使其能够自我识别和发现以前建模工作中的错误，并识别过于微妙或难以手动识别和解释的新物理。这些计算机产生的发现将是加速科学进步的有力工具。通过了解材料和结构的故障是如何发生的，该研究将通过帮助工程师对基础设施，结构部件和设备进行更有效，更强大和更精确的分析设计来促进国家的繁荣和福利。作为赠款的一部分，PI还将促进与纽约市哈莱姆区选定的代表性不足的高中学生和高中教师的指导关系，使未来几代工程师和科学家能够利用人工智能提供的机会。一个新的元建模范式计划自适应地生成模型，以捕捉由于微裂纹，塑性滑移和磨损在子尺度上的微观结构的影响，然后递归地高档响应感兴趣的规模。通过将本构律（理论的或数据驱动的）概念化为有向多重图，即信息流网络，我们将编写本构律的过程理想化为在有向多重图上操作的动作的组合。与奖励函数定义为准确性，鲁棒性，速度和一致性的函数，然后我们发明了一个游戏，其目标是最大限度地提高奖励有限的行动（例如屈服面的形状，硬化规则，损坏，非局部性，细化标准）对约束（例如材料框架无差异，热力学定律）。这种技术，然后应用于层次和并发多尺度耦合模型。在分层模型中，混合机器学习模型可以用作桥接尺度的替代。在并发模型中，使用多相场模型来启用几种类型的模型（理论，数据驱动，混合，均质化-从子规模-模拟），以部署在不同的领域的利益（例如裂纹尖端，剪切带）该奖项反映了NSF的法定使命，并通过使用基金会的智力价值进行评估，更广泛的影响审查标准。

项目成果

Multi‐phase‐field microporomechanics model for simulating ice‐lens growth in frozen soil

• DOI: 10.1002/nag.3408
• 发表时间: 2021-11
• 期刊: International Journal for Numerical and Analytical Methods in Geomechanics
• 影响因子: 4
• 作者: H. S. Suh;WaiChing Sun

An offline multi‐scale unsaturated poromechanics model enabled by self‐designed/self‐improved neural networks

• DOI: 10.1002/nag.3196
• 发表时间: 2021-02
• 期刊: International Journal for Numerical and Analytical Methods in Geomechanics
• 影响因子: 4
• 作者: Y. Heider;H. S. Suh;WaiChing Sun

ILS-MPM: an implicit level-set-based material point method for frictional particulate contact mechanics of deformable particles

• DOI: 10.1016/j.cma.2020.113168
• 发表时间: 2020-01
• 期刊: ArXiv
• 作者: Chuanqi Liu;WaiChing Sun

Sobolev training of thermodynamic-informed neural networks for interpretable elasto-plasticity models with level set hardening

• DOI: 10.1016/j.cma.2021.113695
• 发表时间: 2021-04
• 期刊: Computer Methods in Applied Mechanics and Engineering
• 影响因子: 7.2
• 作者: Nikolaos N. Vlassis;WaiChing Sun

Distance-preserving manifold denoising for data-driven mechanics

• DOI: 10.1016/j.cma.2022.115857
• 发表时间: 2023-01-09
• 期刊: COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
• 影响因子: 7.2
• 作者: Bahmani，Bahador;Sun，WaiChing
• 通讯作者: Sun，WaiChing