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Inicio  /  Algorithms  /  Vol: 16 Par: 4 (2023)  /  Artículo
ARTÍCULO
TITULO

NSGA-PINN: A Multi-Objective Optimization Method for Physics-Informed Neural Network Training

Binghang Lu    
Christian Moya and Guang Lin    

Resumen

This paper presents NSGA-PINN, a multi-objective optimization framework for the effective training of physics-informed neural networks (PINNs). The proposed framework uses the non-dominated sorting genetic algorithm (NSGA-II) to enable traditional stochastic gradient optimization algorithms (e.g., ADAM) to escape local minima effectively. Additionally, the NSGA-II algorithm enables satisfying the initial and boundary conditions encoded into the loss function during physics-informed training precisely. We demonstrate the effectiveness of our framework by applying NSGA-PINN to several ordinary and partial differential equation problems. In particular, we show that the proposed framework can handle challenging inverse problems with noisy data.

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