7 papers · 1 filter
SE(3)-MeanFlow: Few-Step Protein Backbone Generation on Lie Groups
Yikun Bai, Binghang Lu, Yikai Liu +7
Generative modeling of protein backbones promises the de novo design of proteins with prescribed structural and functional properties. Existing diffusion and flow-matching models p…
Muon-OGD: Muon-based Spectral Orthogonal Gradient Projection for LLM Continual Learning
Binghang Lu, Zheyuan Deng, Runyu Zhang +6
A central challenge in continual learning for large language models (LLMs) is catastrophic forgetting, where adapting to new tasks can substantially degrade performance on previous…
AdamFLIP: Adaptive Momentum Feedback Linearization Optimization for Hard Constrained PINN Training
Binghang Lu, Runyu Zhang, Changhong Mou +2
Physics-informed neural networks (PINNs) provide a flexible framework for solving forward and inverse problems governed by partial differential equations (PDEs), but standard PINN…
Muon with Spectral Guidance: Efficient Optimization for Scientific Machine Learning
Binghang Lu, Jiahao Zhang, Guang Lin
Physics-informed neural networks and neural operators often suffer from severe optimization difficulties caused by ill-conditioned gradients, multi-scale spectral behavior, and sti…
Morephy-Net: An Evolutionary Multi-objective Optimization for Replica-Exchange-based Physics-informed Neural Operator Learning Networks
Binghang Lu, Changhong Mou, Guang Lin
We propose an evolutionary Multi-objective Optimization for Replica-Exchange-based Physics-informed operator-learning Networks (Morephy-Net) to solve parametric partial differentia…
iPINNER: An Iterative Physics-Informed Neural Network with Ensemble Kalman Filter
Binghang Lu, Changhong Mou, Guang Lin
Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving forward and inverse problems involving partial differential equations (PDEs) by incorporating p…
NSGA-PINN: A Multi-Objective Optimization Method for Physics-Informed Neural Network Training
Binghang Lu, Christian B. Moya, Guang Lin
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-domi…