Soft Sensor

Soft sensors are virtual sensors that estimate hard-to-measure process variables using readily available data and mathematical models, primarily aiming to improve efficiency and reduce costs in various industries. Current research emphasizes enhancing model stability, interpretability, and data efficiency through techniques like neural networks (including graph neural networks and transformers), physics-informed models, and advanced algorithms such as sequential Monte Carlo and active learning. This field is significant for its potential to optimize industrial processes, improve predictive maintenance, and enable more autonomous systems across diverse applications, from manufacturing and robotics to environmental monitoring.