Parameter Estimation

Parameter estimation, the process of determining the values of model parameters that best fit observed data, is a fundamental problem across numerous scientific disciplines. Current research emphasizes the integration of deep learning techniques, such as convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and physics-informed neural networks (PINNs), with traditional statistical methods like maximum likelihood estimation (MLE) to improve efficiency, robustness, and accuracy, particularly in high-dimensional or complex systems. This focus is driven by the need for more efficient and reliable parameter estimation in diverse applications ranging from financial modeling and gravitational wave astronomy to medical imaging and power system analysis. The development of novel algorithms and model architectures, including those leveraging contrastive learning and Riemannian geometry, aims to address challenges posed by noisy data, high dimensionality, and non-identifiable parameters.