19 citations · 32 across the 8 of their papers we have counts for
8 papers
Machine Learning-Enhanced Aircraft Landing Scheduling under Uncertainties
Yutian Pang, Peng Zhao, Jueming Hu +1
This paper addresses aircraft delays, emphasizing their impact on safety and financial losses. To mitigate these issues, an innovative machine learning (ML)-enhanced landing schedu…
Reinforcement Learning With Reward Machines in Stochastic Games
Jueming Hu, Jean-Raphael Gaglione, Yanze Wang +3
We investigate multi-agent reinforcement learning for stochastic games with complex tasks, where the reward functions are non-Markovian. We utilize reward machines to incorporate h…
Hulk: Graph Neural Networks for Optimizing Regionally Distributed Computing Systems
Zhengqing Yuan, Huiwen Xue, Chao Zhang +1
Large deep learning models have shown great potential for delivering exceptional results in various applications. However, the training process can be incredibly challenging due to…
B-BACN: Bayesian Boundary-Aware Convolutional Network for Crack Characterization
Rahul Rathnakumar, Yutian Pang, Yongming Liu
Accurately detecting crack boundaries is crucial for reliability assessment and risk management of structures and materials, such as structural health monitoring, diagnostics, prog…
Posterior Regularized Bayesian Neural Network Incorporating Soft and Hard Knowledge Constraints
Jiayu Huang, Yutian Pang, Yongming Liu +1
Neural Networks (NNs) have been widely {used in supervised learning} due to their ability to model complex nonlinear patterns, often presented in high-dimensional data such as imag…
Robust physics discovery via supervised and unsupervised pattern recognition using the Euler characteristic
Zhiming Zhang, Yongming Liu
Machine learning approaches have been widely used for discovering the underlying physics of dynamical systems from measured data. Existing approaches, however, still lack robustnes…
Parsimony-Enhanced Sparse Bayesian Learning for Robust Discovery of Partial Differential Equations
Zhiming Zhang, Yongming Liu
Robust physics discovery is of great interest for many scientific and engineering fields. Inspired by the principle that a representative model is the one simplest possible, a new…
Robust Data-Driven Discovery of Partial Differential Equations under Uncertainties
Zhiming Zhang, Yongming Liu
Robust physics (e.g., governing equations and laws) discovery is of great interest for many engineering fields and explainable machine learning. A critical challenge compared with…