The development of geometry processing techniques is proposed for generating higher dimensional functional procedural models of materials from experimentally measured data. This work will use such models to improve functional properties of advanced Lithium-Ion batteries. This work will include: 1) the analysis and use of measured 3D data, 2) creation of novel procedural models, and 3) the development of new machine learning techniques. Efficient simulation techniques will be developed to evaluate the properties and performance characteristics of emerging batteries. First, it will develop techniques for generating and using procedural models that can be applied to a much wider set of domains, to higher dimensions, and to include function of the generated structures. Second, this work will focus on battery design and propose improved battery architectures based on the machine-learning derived performance metrics.
This is a joint project with Purdue University. The main web page is hosted at: http://hpcg.purdue.edu/eager.html