USACM: UQ TTA Webinar. Starting soon. Title: Multifidelity linear regression for scientific machine learning from scarce data

This is a reminder that our next monthly webinar is October 10, 3-4pm ET, is starting soon. 

Monthly Webinar

October 10; 3pm ET

Speaker: Elizabeth Qian, Georgia Tech

Title: Multifidelity linear regression for scientific machine learning from scarce data

Machine learning (ML) methods have garnered significant interest as potential methods for learning surrogate models for complex engineering systems for which traditional simulation is expensive. However, in many scientific and engineering settings, training data are scarce due to the cost of generating data from traditional high-fidelity simulations. ML models trained on scarce data have high variance and are sensitive to vagaries of the training data set. We propose a new multifidelity training approach for scientific machine learning that exploits the scientific context where data of varying fidelities and costs are available; for example high-fidelity data may be generated by an expensive fully resolved physics simulation whereas lower-fidelity data may arise from a cheaper model based on simplifying assumptions. We use the multifidelity data to define new multifidelity control variate estimators for the unknown parameters of linear regression models, and provide theoretical analyses that guarantee accuracy and improved robustness to small training budgets. Numerical results show that multifidelity learned models achieve order-of-magnitude lower expected error than standard training approaches when high-fidelity data are scarce.

Join Zoom Meeting

https://us06web.zoom.us/j/92756548524?pwd=cTFoRXIvNVN4dVFoaHEzK0pQQjhldz09 (Meeting ID: 927 5654 8524/Passcode: 934745)