Physics and Data-driven hybrid modeling
Integrating physics-based modeling techniques (finite element models, ordinary/partial differential equations, state-space models, etc.) and data-driven modeling methods (supervised learning, unsupervised learning, reinforcement learning, and adversarial learning, etc.) to build hybrid models and improve prediction accuracy, generalizability and interpretability.