Highly Accurate Machine Learning Models for Automotive Crash Applications Using CAE Centric AI/ML Platform

In the automotive industry, there have been many efforts of late in using Machine Learning tools to aid crash virtual simulations and further decrease product development time and cost. As the simulation world grapples with how best to incorporate ML techniques, two main challenges are evident. There is the risk of giving flawed recommendations to the design engineer if the training data has some suspect data. In addition, the complexity of porting simulation data back and forth to a Machine Learning software can make the process cumbersome for the average CAE engineer to set up and execute a ML project. We would like to put forth a ML workflow/platform that a typical CAE engineer can use to create training data, train a PINN (Physics Informed Neural Network) ML model and use it to predict, optimize and even synthesize for any given crash problem. The key enabler is the use of an industry first data structure named mwplot that can store diverse types of training data - scalars, vectors, nodal-field and nodal-time-history. This data structure will serve as the go-between of sorts between simulation output and the ML solver. The PINN approach augments the empirical training by checking against governing physics so that any anomaly in the data can be accounted for. All these attributes make for a potent and efficient one-stop ML workflow for the CAE user to be able to set up and execute a high-quality Machine Learning exercise for crash applications.