๐Ÿ‡ธ๐Ÿ‡ฌMeta-Modeling with Bayesian Networks to Facilitate Intelligent Use of Engineering Simulation

Zack Xuereb Conti, Harvard University Graduate School of Design & Singapore University of Technology and Design

Presented at the 6th Annual BayesiaLab Conference in Chicago, November 1-2, 2018.


Abstract For decades, engineers have utilised engineering simulation tools such as finite element analysis to aid consulting architects on how proposed building designs are likely to behave before they are constructed. More recently, the emergence of computational tools in architecture, together with faster simulation algorithms, are enabling architects in the early stages to explore and evaluate a larger variety of design options by searching iteratively through a so-called โ€˜design spaceโ€™. Design space in our context is a multidimensional mathematical space typically bound by the simulation inputs and outputs. When the number of variables defining the design space is more than a handful, it becomes cognitively challenging to draw meaningful inferences on how the input design variables are influencing the simulation response. Consequently, using simulation blindly leads to a shallow understanding of the design space.

In response, we adopt Bayesian networks to compress input/output simulation data into a simulation metamodel whose underlying relationships can be explored. Simulation metamodels are widely used in fields such as aerospace and automotive engineering for quick response prediction. However, most metamodels are typically formulated as forwarding mathematical functions (inputs to output) whose mapping remains difficult to infer global knowledge from when studying multiple variables. Bayesian networks, on the other hand, do not distinguish between inputs and outputs. Thus, the influence between design variables and simulation response can be explored bi-directionally to reveal the important dependencies driving the engineering response over the entire distribution of sampled data points in the design space. Through an applied case study involving structural design, we will illustrate how designers may utilise a Bayesian network metamodel to reveal valuable insight into practice.

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