Binghang Lu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Binghang Lu has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Guang Lin
School of Mechanical Engineering, Purdue University, West Lafayette, USA
4 shared publications - Changhong Mou
Department of Mathematics and Statistics Utah State University Logan Utah USA
2 shared publications - Christian Moya
Department of Mathematics Purdue University 47907 West Lafayette IN USA
2 shared publications
Latest Publications
- 13 December 2025
- Published by Elsevier in Journal of Computational Physics
- Vol. 548
iPINNER: An iterative physics-informed neural network with ensemble Kalman filter
- 13 December 2025
- Published by Elsevier in Journal of Computational Physics
- Vol. 548
iPINNER: An iterative physics-informed neural network with ensemble Kalman filter
- Open Access
- 3 April 2023
- Published by MDPI AG in Algorithms
- Vol. 16 (4) , 194
NSGA-PINN: A Multi-Objective Optimization Method for Physics-Informed Neural Network Training
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.
- 3 April 2023
- Published by MDPI AG in Algorithms
- Vol. 16 (4) , 194
Open AccessNSGA-PINN: A Multi-Objective Optimization Method for Physics-Informed Neural Network Training
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.
- Open Access
NSGA-PINN: A Multi-Objective Optimization Method for Physics-Informed Neural Network Training
This paper presents NSGA-PINN, a multi-objective optimization framework for 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.
Open AccessNSGA-PINN: A Multi-Objective Optimization Method for Physics-Informed Neural Network Training
This paper presents NSGA-PINN, a multi-objective optimization framework for 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.