This repository allows you to solve forward and inverse problems related to partial differential equations (PDEs) using finite basis physics-informed neural networks (FBPINNs). To improve the ...

The world today is marked by paradox. Never before has humanity possessed such extraordinary scientific knowledge, ...

Abstract: Nonlinear equation systems are ubiquitous in a variety of fields, and how to tackle them has drawn much attention, especially dynamic ones. As a particular class of recurrent neural network, ...

Abstract: Physics-informed neural networks (PINNs) incorporate physical constraints into their loss functions, allowing them to efficiently solve Partial Differential Equations (PDEs). In this work, ...