A cooperative game for automated learning of elasto-plasticity knowledge graphs and models with AI-guided experimentation

Abstract

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.

References in corpus (6)

• Data-driven computational mechanics
• Bayesian Reinforcement Learning: A Survey
• Data Driven Computing with Noisy Material Data Sets
• The role of mentorship in protege performance
• Meta-modeling game for deriving theoretical-consistent, micro-structural-based traction-separation laws via deep reinforcement learning
• Stress-induced anisotropy in granular materials: fabric, stiffness, and permeability

Cited by in corpus (5)

• A Physics-informed Assembly of Feed-Forward Neural Network Engines to Predict Inelasticity in Cross-Linked Polymers
• Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlook
• Geometric deep learning for computational mechanics Part II: Graph embedding for interpretable multiscale plasticity
• General solutions for nonlinear differential equations: a rule-based self-learning approach using deep reinforcement learning
• Towards out of distribution generalization for problems in mechanics