A cooperative game for automated learning of elasto-plasticity knowledge graphs and models with AI-guided experimentation
This work addresses material science challenges by automating modeling and experimentation, though it appears incremental in applying existing AI techniques to a specific domain.
The paper tackles the problem of predicting constitutive responses of elasto-plastic materials by introducing a multi-agent meta-modeling game that generates data, knowledge, and models, resulting in automated optimization of predictions through deep reinforcement learning.
We introduce a multi-agent meta-modeling game to generate data, knowledge, and models that make predictions on constitutive responses of elasto-plastic materials. We introduce a new concept from graph theory where a modeler agent is tasked with evaluating all the modeling options recast as a directed multigraph and find the optimal path that links the source of the directed graph (e.g. strain history) to the target (e.g. stress) measured by an objective function. Meanwhile, the data agent, which is tasked with generating data from real or virtual experiments (e.g. molecular dynamics, discrete element simulations), interacts with the modeling agent sequentially and uses reinforcement learning to design new experiments to optimize the prediction capacity. Consequently, this treatment enables us to emulate an idealized scientific collaboration as selections of the optimal choices in a decision tree search done automatically via deep reinforcement learning.