AI for inverse problems in solid mechanics

逆问题使用系统响应的测量值来计算系统及其输入的未知值。当存在关于系统的有限信息、未知输入、多个解决方案或实际上不存在解决方案时，这是有利的。从本质上讲，您从失败的东西开始，并使用从中获得的信息来确定失败条件。未知的属性随处可见。所有看似定义明确的材料属性都是分布的。在发电、运输和建筑中，许多安全关键的工业应用程序都有复杂的加载情况，这些情况没有完全定义。因此，应用反问题技术将为改进这些领域的设计提供优势。在多个领域拥有多个未知数会产生计算需求。正因为如此，使用反问题技术通常被摒弃。因此，我将通过高性能计算机(HPC)利用可用的最高形式的计算能力。为了建立高性能混凝土的模型，我将考虑固体力学理论、数学理论、计算理论和软件开发。一旦建立了基本模型，将通过基准和分析进行优化过程。人工智能(AI)的增强将是该项目不可或缺的一部分。使用为人工智能设计的统计方法可以用来优化和减少解决方案所需的迭代次数。固体力学研究小组拥有促进这一项目的专业知识。Anton Shterenlikht博士在HPC方法方面拥有广泛的专业知识。他在FORTRAN粗略方面的知识对这个项目将是无价的。

Inverse problems use measurements from a system response to calculate unknown values for the system and its inputs. This is advantageous when there is limited information about the system, unknown inputs, multiple solutions or indeed no solution exists. Essentially, you start with something that has failed and use information from this to work out the failure conditions. Unknown properties are everywhere. All seemingly well-defined material properties are distributions. Many safety critical industrial applications in power generation, transport and construction, have complex loading cases that are not fully defined. Therefore, applying the inverse problem technique will present an advantage for improving designs in these areas. Having multiple unknowns in multiple areas creates computational demand. Due to this, using the inverse problem technique is often dismissed. Thus, I will be utilising the highest form of computational power available through High Performance Computers (HPC). To build a model for HPC, I will be considering solid mechanic theory, mathematical theory, computational theory and software development. Once a basic model has been built, an optimisation process will take place through benchmarking and profiling. The augmentation of artificial intelligence (AI) will be an integral part of the project. Using the statistical methods designed for AI can be used to optimise and reduce the number of iterations required for a solution. The Solid Mechanics Research Group has the expertise and knowledge to facilitate this project. Dr. Anton Shterenlikht has a wide expertise in HPC methods. His knowledge of with FORTRAN coarrys will be invaluable for this project.