Discretization Error

Discretization error arises when continuous data or processes are represented using discrete values, leading to inaccuracies in models and analyses. Current research focuses on mitigating this error in various applications, including neural network training (e.g., differentiable neural architecture search and physics-informed neural networks), computational fluid dynamics, and demographic prediction. Understanding and reducing discretization error is crucial for improving the accuracy and reliability of numerous scientific computations and machine learning models, particularly where continuous data is inherently involved. This is especially important in applications with high stakes, such as demographic modeling where bias introduced by discretization can have significant real-world consequences.