  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.
