1. NSGA-PINN: A Multi-Objective Optimization Method for Physics-Informed Neural Network Training.
- Author
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Lu, Binghang, Moya, Christian, and Lin, Guang
- Subjects
- *
OPTIMIZATION algorithms , *INVERSE problems , *PARTIAL differential equations , *ORDINARY differential equations , *SCIENTIFIC computing - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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