This presentation was held at the 2020 NAFEMS UK Conference "Inspiring Innovation through Engineering Simulation". The conference covered topics ranging from traditional FEA and CFD, to new and emerging areas including artificial intelligence, machine learning and EDA.

Resource Abstract

Today’s engineer is tasked with a myriad of different computational tasks for which they turn to a variety of proprietary and open-source computational tools. These tasks include design optimisation, uncertainty quantification and statistical inference. For the open-source codes, the onus of understanding the computational methodology resides wholly with the engineer, while for proprietary codes, the onus is on the software vendor to elucidate their techniques. Within today’s rapidly evolving ecosystem—owing to the rise of machine learning—it’s not surprising that companies are increasingly gravitated towards open-source tools as they are better reflective of the state-of-the-art. However, these codes need to have better documentation, appropriate version control mechanisms, and avenues for educating engineers to be truly transformative in the computational simulation sector. Effective Quadratures aims to do precisely this.

Effective Quadratures is an open-source library for uncertainty quantification, machine learning, optimisation, numerical integration and dimension reduction – all using orthogonal polynomials. It is particularly useful for models / problems where output quantities of interest are smooth and continuous; to this extent it has found widespread applications in computational engineering models (finite elements, computational fluid dynamics, etc). It is built on the latest research within these areas and has both deterministic and randomized algorithms. Unlike existing open-source tools built on neural networks, Effective Quadratures is built by projecting polynomials and splines over subspaces —ensuring efficacious use of black-box model evaluations. Effective Quadratures is actively being developed by researchers at the University of Cambridge, Imperial College London, Stanford University, The University of Utah, The Alan Turing Institute and the University of Cagliari.

In this presentation, I will talk about some of the underlying methodologies in the code and show how easy it is to use the underlying subroutines: within just a few lines of code, users can build sophisticated models, optimise complex functions and get statistical summaries of their problem. I will wrap up with pertinent industrial case studies.