Breaking the curse of dimensionality : from large data to reduced order models
Date/Time:22 Jan 2020 16:00Venue: S17 #05-11 SR5Speaker: Lucas Lestandi, Nanyang Technological UniversityBreaking the curse of dimensionality : from large data to reduced order models.It is known to every scientist who works with numerical simulation – that increasing complexity in the computing task leads to exploding computing times. This can then be materialized in many ways e.g. very fine meshes, extremely small time steps, multiscale or multiphysics problems and most importantly, large number of dimensions. The last issue (large of dimensions) is often referred as the curse of dimensionality. It is very common in fields as diverse as finance, chemistry or “simple” mechanical engineering optimization problem, parametric studies, etc. In this talk, we will cover some of the essential aspects in building reduced order models that can overcome some of the above difficulties. First, we introduce low rank approximation of high dimensional data. These tools enable both data reduction (by several orders) and yield reduced bases that are used to build the actual reduced order model (ROM). Then we will see how common ROMs are built and the issues that remain to be solve. Finally, we will have a look at the future of ROM, in which deep learning is set to play a large part.Add to calendar:
