AI for inverse problems in solid mechanics

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.