Multiphysics-Informed Neural Network (MPINN) for Accurate Multidimensional Predictions of Mechatronic Systems
Abstract
Mechatronic systems are often characterized by complex multiphysics phenomena that are difficult to model using traditional analytical or computational methods. Deep neural networks (DNNs) have shown great potential in predicting the behavior of these systems, but their accuracy and robustness are heavily dependent on the availability of large amounts of data. In this proposal, we introduce a novel architecture for a multiphysics-informed neural network (MPINN) that can accurately and efficiently predict the multidimensional responses of mechatronic systems, even with limited data.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Feb 16, 2024
- Source ID
- FA23862314094
Entities
People
- Ki Yong Oh
Organizations
- Air Force Office of Scientific Research
- United States Air Force