The AI industry is experiencing a significant shift that is not yet widely recognized. While large language models (LLMs) ...

This article investigates the problem of fault estimation in nonlinear uncertain manipulator systems with time-delay. A novel fault estimation scheme is proposed, which optimizes the iterative ...

This article proposes a novel data-driven iterative learning decoupling controller aimed at addressing the precise tracking issues in discrete-time nonlinear multi-input-multioutput (MIMO) repetitive ...

Learning the underlying physics of complex systems is a fundamental step in advancing data-driven modeling and engineering design. Symbolic regression (SR) is crucial for deriving data-driven, ...

Learning from complex, multidimensional data has become central to computational mathematics, and among the most successful high-dimensional function approximators are deep neural networks (DNNs).

In our experiments, we found that graph neural networks generally outperformed structure agnostic models and excel in generalization on unseen organisms, implying a potential path to generalizable ...