Research project funded by the Natural Science Foundation of Guangdong Province-General Program
Uncertainty quantification and spatiotemporal causal discovery for reliable traffic prediction
Research paper accepted by Reliability Engineering and Systems Safety
Multi-state systems (MSS) are widely used for modeling the behavior of engineering applications, where the system and its components can have more than two distinct states. Physics-Informed Neural Networks (PINNs) offer a viable solution for characterizing the dynamic state evolution of MSS. However, existing methods predominantly rely on uniformly sampled collocation points across the problem domain when training PINNs. Although some residual-based active learning methods exist, they are inherently static and local, and often fail to capture a crucial aspect of PINN training: identification and accurate modeling of the “critical transition regions” within the problem domain. To address this fundamental challenge, we treat PINN as a dynamic system and introduce a novel active learning method grounded in chaos theory to identify regions within the problem domain that are highly sensitive to initial conditions. Specifically, our method quantifies the degree of chaos at candidate collocation points by introducing small perturbations and using PINN’s forward propagation to simulate the dynamic evolution of both the original and perturbed collocation points. Collocation points that exhibit pronounced chaotic behavior—- where evolutionary trajectories diverge rapidly following perturbation—are identified as the system’s most unstable and valuable regions for PINN training. By prioritizing these dynamically unstable points, our method directs PINN to focus its learning on accurately delineating the boundaries of state transitions, thereby significantly enhancing the accuracy of reliability analysis. Experimental results on multiple benchmark partial differential equations (PDEs) and several MSSs demonstrate that, compared to other PINN learning schemes, our method shows superior accuracy and computational efficiency in MSS reliability assessment.
Prof. Cheng-Lin Liu gave a talk on “Open-World Learning: Problems and Strategies”
Traditional methods of pattern classification and machine learning usually assume closed world: the input pattern falls within a fixed set of classes. However, in open world, the input pattern can be of either known or unknown classes, or be outlier. While in training, the data may emerge incrementally, and the new dataset contain samples or with known or unknown classes, either labeled or unlabeled, or be outlier. Such open-world learning scenario involves multiple challenges including out-of-distribution (OOD) detection, confidence estimation, unlabeled data exploitation, catastrophic forgetting and novel category discovery. The challenges are attacked by combining techniques such as generative modeling, regularization, knowledge distillation, and hybrid learning. This talk will outline the status of open-world pattern recognition, identify the main challenges of open-world learning and main strategies, and present some recent progress achieved in my group: open-set recognition, class-incremental learning, and generalized category discovery.
