11 papers
Meta-Inverse Physics-Informed Neural Networks for High-Dimensional Ordinary Differential Equations
Zhao Wei, Kenneth Hor Cheng Koh, Sheng Yuan Chin +3
Solving inverse problems in dynamical systems governed by high-dimensional coupled ordinary differential equations (ODEs) is a ubiquitous challenge in scientific machine learning.…
Automated Large-scale CVRP Solver Design via LLM-assisted Flexible MCTS
Tong Guo, Caishun Chen, Yew Soon Ong
Solving large-scale CVRP (LSCVRP) with hundreds to thousands of nodes remains difficult for even state-of-the-art solvers. Divide-and-conquer can scale by decomposing the instance…
Transferable Physics-Informed Representations via Closed-Form Head Adaptation
Jian Cheng Wong, Isaac Yin Chung Lai, Pao-Hsiung Chiu +3
Physics-informed neural networks (PINNs) have garnered significant interest for their potential in solving partial differential equations (PDEs) that govern a wide range of physica…
TransGP: Task-Conditioned Transformer-Guided Genetic Programming for Multitask Dynamic Flexible Job Shop Scheduling
Meng Xu, Jiao Liu, Hua Yu +1
Hyper-heuristics have become a popular approach for solving dynamic flexible job shop scheduling (DFJSS) problems. They use gradient-free optimization techniques like Genetic Progr…
GRASP: Gradient Realignment via Active Shared Perception for Multi-Agent Collaborative Optimization
Sihan Zhou, Tiantian He, Yifan Lu +2
Non-stationarity arises from concurrent policy updates and leads to persistent environmental fluctuations. Existing approaches like Centralized Training with Decentralized Executio…
Taming the Instability: A Robust Second-Order Optimizer for Federated Learning over Non-IID Data
Yuanqiao Zhang, Tiantian He, Yuan Gao +5
In this paper, we present Federated Robust Curvature Optimization (FedRCO), a novel second-order optimization framework designed to improve convergence speed and reduce communicati…