8 papers
CONTRAMEM: Learning Self-Evolving Procedural Memory from Contrasting Multi-Model Trajectories
Zheyuan Deng, Binghang Lu, Hanqi Feng +11
Autonomous computer-use agents are increasingly applied to long-horizon tasks requiring coordinated application calls, persistent state tracking, and verifier-sensitive writes, yet…
Hybrid SINDy-EnKF in Learning Chikungunya Dynamics from Incomplete, Noisy or Partially Observed Data
Bernard Asamoah Afful, Changhong Mou, Luis Gordillo
Current mechanistic models for the transmission dynamics of the Chikungunya virus (CHIKV) rely on uncertain parameters or partially observed data. This limitation challenges the us…
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…
Neural-POD: A Plug-and-Play Neural Operator Framework for Infinite-Dimensional Functional Nonlinear Proper Orthogonal Decomposition
Changhong Mou, Binghang Lu, Guang Lin
AI for science (AI4Science) models often suffer from discretization: learned representations remain tied to the training grid, limiting transfer across resolutions, solvers and app…
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…
Energy-Dissipative Evolutionary Kolmogorov-Arnold Networks for Complex PDE Systems
Guang Lin, Changhong Mou, Jiahao Zhang
We introduce evolutionary Kolmogorov-Arnold Networks (EvoKAN), a novel framework for solving complex partial differential equations (PDEs). EvoKAN builds on Kolmogorov-Arnold Netwo…