Engineering Dataset

Engineering datasets are collections of data used to train and evaluate machine learning models for solving engineering problems, ranging from predicting material behavior to analyzing complex systems like fluid dynamics. Current research focuses on developing and benchmarking various model architectures, including neural networks (feedforward, deep operator regression, and transformers), Gaussian processes, and ensemble methods, to improve accuracy, efficiency, and robustness in diverse applications. These efforts are driven by the need for more reliable and efficient data-driven solutions in engineering, impacting areas such as design optimization, predictive maintenance, and safety-critical systems. The development of comprehensive benchmark suites and exploration of active learning techniques are also key areas of focus to address challenges like data scarcity and high annotation costs.