pyMOR - Sustainable Software for Model Order Reduction

pyMOR - 用于减少模型阶数的可持续软件

• 批准号: 391057179
• 负责人: Dr. Stephan Rave
• 金额: --
• 依托单位: Max-Planck-Institut für Dynamik komplexer technischer Systeme
• 依托单位国家: 德国
• 项目类别: Research data and software (Scientific Library Services and Information Systems)
• 财政年份: 2018
• 资助国家: 德国
• 起止时间: 2017-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/391057179?language=en
• 关键词: pyMOR; Sustainable; Software; Model; Order

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 fraction of 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 (http://pymor.org/), we have developed a powerful 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 research 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, software demonstration, teaching or large-scale research applications.Second, we will develop guidelines for unit testing of research software in the field of scientific computing. These guidelines will help developers to systematically write comprehensive unit tests for their software, assuring 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.

用偏微分方程描述的数学模型的数值模拟是当今几乎所有科学学科的重要研究工具。然而，这种模型的使用往往是有限的可用的计算资源。在过去的十年中，各种算法已经开发出计算，对于一个给定的数值PDE模型，一个数学上认证的代理，可以模拟在一个小部分的时间所需的原始模型的解决方案。这些技术，称为模型降阶（莫尔），现在正在成为许多仿真工作流程中不可或缺的一部分，否则即使在最大的超级计算机上也是不可行的。通过py莫尔（http：//pymor.org/），我们开发了一个功能强大的莫尔软件库，它可以很容易地与PDE求解器库集成，允许将莫尔技术应用于使用这些库实现的PDE模型。本项目的主要目标是开发基础设施，以支持py莫尔和相关研究软件的可持续开发和部署。首先，我们将开发和部署一个面向研究的云服务，该服务将提供基于应用程序容器的统一开发，持续交付和部署工作流程。研究软件将由这项服务提供的各种用例，如持续集成，软件演示，教学或大规模的研究应用程序。第二，我们将制定在科学计算领域的研究软件的单元测试指南。这些指导原则将帮助开发人员系统地为他们的软件编写全面的单元测试，确保他们的产品的质量和长期的可维护性。基于这些基础设施措施，我们将提高py莫尔的可用性，使我们的软件成为一个通用的莫尔工具，用于各种基于PDE的科学计算应用。