TSONN: Time-stepping-oriented neural network for solving partial differential equations

1 code implementation • 25 Oct 2023 • Wenbo Cao, Weiwei Zhang

Deep neural networks (DNNs), especially physics-informed neural networks (PINNs), have recently become a new popular method for solving forward and inverse problems governed by partial differential equations (PDEs).

Machine Learning of Partial Differential Equations from Noise Data

1 code implementation • 28 Sep 2020 • Wenbo Cao, Weiwei Zhang

Machine learning of partial differential equations from data is a potential breakthrough to solve the lack of physical equations in complex dynamic systems, but because numerical differentiation is ill-posed to noise data, noise has become the biggest obstacle in the application of partial differential equation identification method.

BIG-bench Machine Learning