Surrogate Models for Efficient Multiscale Modeling of Composite Materials
Description
A custom neural network architecture containing graph convolutional network (GCN) and long short-term memory (LSTM) layers was trained as a computationally efficient surrogate for a physics-based composite modeling simulation.
Detailed example
Predict constitutive behavior as a function of material properties and applied strain.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
Surrogate models are attractive because they can be evaluated many orders of magnitude faster than physics-based models and with a high degree of accuracy. Such models can be used for efficient multiscale modeling, design optimization, Monte Carlo methods, and optimal experimental design in ways that would be intractable with many physics-based models.
Controls / human review
ATO: Not reported; PIA: Not published