A novel deep learning modeling approach guided by mesoscience—MGDL
Deep learning modeling that incorporates physical knowledge is currently a hot topic, and a number of excellent techniques have emerged. The most well-known one is the physics-informed neural networks (PINNs). PINN integrates the residuals of the system’s governing partial differential equations (PDEs) and the initial value/boundary conditions into the loss function, thus the resulting model satisfies the constraints of the physical laws represented by the PDEs. However, PINN cannot work if equations among the key physical quantities of the system have not been established. To model such systems, novel methods must be developed. MGDL (mesoscience-guided deep learning, a deep learning modeling approach guided by mesoscience, was proposed by Li Guo et al. from Institute of Process Engineering (IPE), Chinese of Academy Sciences (CAS). Mesoscience is a methodology for tackling multilevel complexities. It focuses on the study of mesoscale problems at different levels and correlates t...