Presentations

Machine Learning for Modelling and Decision Making in Complex Physical Domains, at University College Dublin, Dublin, Ireland, Wednesday, September 25, 2019
My lab at the University of Waterloo carries out work on a variety of topics within Artificial Intelligence and Machine Learning with a focus on using real world problems to discover computationally hard challenges for modelling of uncertainty, dealing with large or streaming data, learning predictive models and enabling decision making.  The methods we focus on include Deep Reinforcement Learning, Convolutional and Recurrent Neural Networks, Ensemble Tree methods and manifold based data/dimensionality reduction analysis. Motivation for our work comes from domains such as automotive,... Read more about Machine Learning for Modelling and Decision Making in Complex Physical Domains
Compact Representation of a Multi-dimensional Combustion Manifold Using Deep Neural Networks, at European Conference on Machine Learning (ECML 2019), Wurzburg, Germany, Thursday, September 19, 2019:
ecml2019_talk.pdf

The computational challenges in turbulent combustion simulations stem from the physical complexities and multi-scale nature of the problem which make it intractable to compute scale-resolving simulations. For most engineering applications, the large scale separation between the flame (typically sub-millimeter scale) and the characteristic turbulent flow (typically centimeter or meter scale) allows us to evoke simplifying assumptions–such as done for the flamelet model–to pre-compute all the chemical reactions and map them to a low-order manifold. The resulting manifold is then tabulated...

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