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

这项学院早期职业发展(Career)计划拨款将支持基础研究，利用人工智能/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。虽然在脆性断裂、应变局部化和延性流动等各种破坏机制的基本理解方面已经取得了相当大的进展，但最近实验技术的进步，如数字图像相关和微计算机断层成像，导致了关于破坏的大量数据的涌入，而这些数据并不总是容易手动合并到模型中。目前的努力利用了人工智能重复生成和测试假说的能力，这样它就可以自我识别和发现以前建模工作中的错误，并识别出过于微妙或难以手动识别和解释的新物理。这些由计算机产生的发现将成为加速科学进步的有力工具。通过了解材料和结构失效是如何发生的，这项研究将帮助工程师对基础设施、结构部件和设备进行更高效、更稳健和更精确的分析设计，从而促进国家繁荣和福利。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。一个新的元建模范例计划自适应地生成模型，以捕捉由于次尺度上的微裂纹、塑料滑移和磨损而演变的微结构的影响，然后递归地对感兴趣的尺度做出更高层次的响应。通过将本构定律(理论或数据驱动的)概念化为有向多重图，即信息流网络，我们将本构关系的编写过程理想化为在有向多重图上操作的动作的组合。通过将奖励函数定义为精度、稳健性、速度和一致性的函数，我们发明了一个博弈，其目标是通过针对约束(例如材料-框架无关性、热力学定律)的有限动作(例如屈服面形状、硬化规则、损伤、非局部性、细化准则)来最大化奖励。然后将该技术应用于分层和并发的多尺度耦合模型。在层次模型中，可以使用混合机器学习模型作为桥接尺度的代理。在并发模型中，多相场模型用于使几种类型的模型(理论模型、数据驱动模型、混合模型、同质化模型-从子尺度模拟)能够部署在不同的感兴趣领域(例如，裂缝尖端、剪切带)以产生最准确的模拟。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.