The evaluation of constitutive models, especially for high-risk and high-regret engineering applications, requires efficient and rigorous third-party calibration, validation and falsification. While there are numerous efforts to develop paradigms and standard procedures to validate models, difficulties may arise due to the sequential, manual and often biased nature of the commonly adopted calibration and validation processes, thus slowing down data collections, hampering the progress towards discovering new physics, increasing expenses and possibly leading to misinterpretations of the credibility and application ranges of proposed models. This work attempts to introduce concepts from game theory and machine learning techniques to overcome many of these existing difficulties. We introduce an automated meta-modeling game where two competing AI agents systematically generate experimental data to calibrate a given constitutive model and to explore its weakness, in order to improve experiment design and model robustness through competition. The two agents automatically search for the Nash equilibrium of the meta-modeling game in an adversarial reinforcement learning framework without human intervention. By capturing all possible design options of the laboratory experiments into a single decision tree, we recast the design of experiments as a game of combinatorial moves that can be resolved through deep reinforcement learning by the two competing players. Our adversarial framework emulates idealized scientific collaborations and competitions among researchers to achieve a better understanding of the application range of the learned material laws and prevent misinterpretations caused by conventional AI-based third-party validation.

中文翻译：

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一种非合作元建模游戏，用于自动第三方校准、验证和伪造具有并行对抗性攻击的本构法

本构模型的评估，特别是对于高风险和高遗憾的工程应用，需要高效、严格的第三方校准、验证和证伪。虽然在开发范式和标准程序来验证模型方面付出了很多努力，但由于普遍采用的校准和验证过程的顺序性、手动性和经常有偏见的性质，可能会出现困难，从而减慢数据收集速度，阻碍发现新物理学的进展，增加费用并可能导致对建议模型的可信度和应用范围的误解。这项工作试图引入博弈论和机器学习技术中的概念，以克服这些现有的许多困难。我们引入了一个自动元建模游戏，其中两个相互竞争的 AI 代理系统地生成实验数据以校准给定的本构模型并探索其弱点，以便通过竞争改进实验设计和模型鲁棒性。这两个代理自动在对抗性强化学习框架中搜索元建模游戏的纳什均衡，而无需人工干预。通过将实验室实验的所有可能设计选项捕获到单个决策树中，我们将实验设计重新定义为组合动作的游戏，可以通过两个竞争参与者的深度强化学习来解决。