Simulation modelling inverse problems of partial differential equations with physics-informed neural networks

Key Points

• The central aim is to explore how physics-informed neural networks can solve inverse problems in simulation modelling of partial differential equations.
• Utilized simulation modelling techniques to address inverse problems.
• Employed physics-informed neural networks for problem-solving.
• Analyzed the efficiency and accuracy of the proposed methods.
• Demonstrated improved accuracy in solving inverse problems compared to traditional methods.
• Showed that neural networks can effectively integrate physical laws in simulations.

Abstract