  In this work, geometry optimization of mechanical truss using computer-aided
finite element analysis is presented. The shape of the truss is a dominant
factor in determining the capacity of load it can bear. At a given parameter
space, our goal is to find the parameters of a hull that maximize the
load-bearing capacity and also don't yield to the induced stress. We rely on
finite element analysis, which is a computationally costly design analysis tool
for design evaluation. For such expensive to-evaluate functions, we chose
Bayesian optimization as our optimization framework which has empirically
proven sample efficient than other simulation-based optimization methods.
  By utilizing Bayesian optimization algorithms, the truss design involves
iteratively evaluating a set of candidate truss designs and updating a
probabilistic model of the design space based on the results. The model is used
to predict the performance of each candidate design, and the next candidate
design is selected based on the prediction and an acquisition function that
balances exploration and exploitation of the design space. Our result can be
used as a baseline for future study on AI-based optimization in expensive
engineering domains especially in finite element Analysis.
