Parameter Identification

Parameter identification focuses on determining the optimal values of model parameters that best fit observed data, a crucial step in validating and utilizing mathematical models across diverse scientific disciplines. Current research emphasizes robust methods for handling noisy or incomplete data, exploring techniques like Bayesian optimization, physics-informed neural networks, and deep learning architectures (including convolutional neural networks and recurrent neural networks) to improve accuracy and efficiency. These advancements are significantly impacting fields ranging from climate modeling and battery technology to robotics and systems biology, enabling more accurate predictions, improved control systems, and a deeper understanding of complex systems.