Noisy Measurement

Noisy measurement is a pervasive challenge across diverse scientific fields, hindering accurate data analysis and model building. Current research focuses on developing robust algorithms and models, including physics-informed neural networks, Kalman filters (both classical and deep learning variants), and various gradient descent methods, to effectively reconstruct signals and parameters from incomplete or corrupted data. These advancements are crucial for improving the reliability and accuracy of scientific inferences in areas ranging from fluid dynamics and quantum computing to sensor networks and medical imaging, ultimately leading to more precise and trustworthy results.