Hybrid workflow of Simulation and Deep Learning on HPC: A Case Study for Material Behavior Determination

Nowadays, machine learning (ML), especially deep learning(DL) methods, provide ever more real-life solutions. However, the lack of training data is often a crucial issue for these learning algorithms, the performance accuracy of which relies on the amount and the quality of the available data. This is particularly true when applying ML/DL based methods for specific areas e.g. material characteristics identification, as it requires huge cost of time and manual power getting observational data from real life. In the mean while, simulations on HPC have already been commonly used in computational science due to the fact that it has the ability of generating sufficient and noise free data, which can be used for training the ML/DL based models. However, in order to achieve accurate simulation results the input parameters usually have to be determined and validated by a large number of tests. Furthermore, the evaluation and validation of such input parameters for the simulation often require a deep understanding of the domain specific knowledge, software and programming skills, which can in turn be solved by ML/DL based methods. In this paper, a novel hybrid workflow combining a multi-task neural network and the simulation on high performance computers(HPC) is proposed, which can address the problem of data sparsity and reduce the demand for expertise, resources, and time in determining the validated parameters for simulation. This work is demonstrated through experiments on determination of material behaviors, and the results prove a promising performance (MSE = 0.0386) through this workflow.