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Data-driven Approaches for Structural Optimisation

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If you have a question about this talk, please contact Mishael Nuh.

Structural optimisation is an integrated tool in the design process and is used to obtain more efficient structures. Popularly used finite element models are computationally expensive and lacking gradient information. Deep learning is a popular subset of machine learning, which is a construction of neural networks. Providing physics information to the model makes the “black-box” neural network model into a “grey-box” one, hence turns the model into a physics-based one. This study aims to explore possible structural optimisation techniques using deep neural networks.

This talk is part of the Engineering Department Structures Research Seminars series.

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