Data Fitting

Data fitting aims to find mathematical models that accurately represent observed data, a crucial task across numerous scientific disciplines. Current research emphasizes developing efficient and accurate algorithms, focusing on novel neural network architectures and improved optimization techniques like damped Newton methods and combined activation functions to handle complex relationships and high-dimensional data. These advancements improve the speed and accuracy of fitting diverse data types, from simple mathematical expressions to complex time series and multiscale systems, impacting fields ranging from machine learning and signal processing to motor neuroscience and partial differential equation modeling.