  Topologically interlocking architectures can generate tough ceramics with
attractive thermo-mechanical properties. This concept can make the material
design pathway a challenging task, since modeling the whole design space is
neither effective nor feasible. We propose an approach to design
high-performance architectured ceramics using machine learning (ML) with data
from finite element analysis (FEA). Convolutional neural networks (CNNs) and
Multilayer Perceptrons (MLPs) are used as the deep learning approaches. A
limited set of FEA simulation data containing a variety of architectural design
parameters is used to train our neural networks, including learning how
independent and dependent design parameters are related. A trained network is
then used to predict the optimum structure from the configurations. A FEA
simulation is run on the best predictions of both MLP and CNN algorithms to
evaluate the performance of our networks. Although a limited amount of
simulation data are available, our networks are effective in predicting the
transient thermo-mechanical responses of possible panel designs. For example,
the optimal design after using CNN prediction resulted in $\approx \! 30\%$
improvement in terms of edge temperature.
