pyMOR - Sustainable Software for Model Order Reduction (pyMOR)

Basic data for this project

Type of project: Individual project
Duration: 01/03/2018 - 31/03/2022


The numerical simulation of mathematical models described by partial differential equations (PDEs) is nowadays an important tool for research in almost every scientific discipline. Yet, the use of such models is often limited by the available computational resources. Over the last decade, a variety of algorithms have been developed which compute, for a given numerical PDE model, a mathematically certified surrogate that can be simulated in a small fractionof the time required for the solution of the original model. These techniques, known as model order reduction (MOR), are now becoming an integral part in many simulation workflows which otherwise would be infeasible, even on the largest available supercomputers. With pyMOR, we have developed apowerful MOR software library which can be easily integrated with PDE solver libraries, allowing application of MOR techniques to PDE models implemented with these libraries.The main goal of this project is the development of infrastructures to support the sustainable development and deployment of pyMOR and related resarch software. First, we will develop and deploy a research oriented cloud service which will offer a unified development, continuous delivery and deployment workflow based on application containers. Research software will be delivered by this service for various use cases, such as continuous integration, softwaredemonstration, teaching or large-scale research applications. Second, we will develop guidelines for unit testing of research software in the field of scientificcomputing. These guidelines will help developers to systematically write comprehensive unit tests for their software, aussuring the quality and long-term maintainability of their product.Based on these infrastructural measures, we will improve pyMOR's usability to establish our software as a universal MOR tool for various PDE-based scientific computing applications.

Keywords: Model order reduction; Python; pymor