| Summary: | Computational simulation of mechanical structures is an important tool for the design and optimization
of structural components and the control of new processes. However, for simulations to be predictive,
it is necessary to generate models that can accurately describe the mechanical properties of materials
under different states of stress. These constitutive laws are often determined on an ad-hoc basis, by
fitting the results of standard mechanical tests to phenomenological models.
In this Final Year Project, machine learning tools including Genetic Algorithm are utilized to develop
a data-driven model for materials behaviour. We compare these models with conventionally fitted ones
derived for analytical formulations. We test our strategy by fitting these models to experimental data
from a historical dataset for rubber. The fitting strategy of our model is to take data from one stress state
for training and validate our model by comparing its performance in two other stress states.
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