3D generative design with Monolith AI

Deep generative models are machine learning techniques capable of automatically abstracting information from a high-dimensional domain without the bias of a human user. These techniques enable the generation of efficient geometric representations which can be utilised for a variety of purposes such as the prediction of performance quantities or optimisation of geometry.

This presentation will cover use cases of how Monolith AI has deployed these types of models to solve our clients' problems. Despite the flexibility of such models; overcoming their inherent black-box nature is, we believe, the key to wider adoption in the engineering domain. This presentation will cover some of the research directions we are working on to help overcome these limitations. Presented by Peter Wooldridge at AAAI 2022 ADAM Workshop.‍