Interpolation Regime

Interpolation regime research focuses on developing methods to accurately estimate function values at unseen points based on known data, addressing challenges in diverse fields from machine learning to scientific simulations. Current research emphasizes efficient algorithms for high-dimensional data, exploring architectures like neural networks, Gaussian processes, and graph neural networks, often incorporating physics-informed constraints or active sampling strategies to improve accuracy and computational efficiency. This work is significant for improving the accuracy and speed of predictions in various applications, ranging from image enhancement and video generation to materials science and weather forecasting, ultimately leading to more robust and reliable models across scientific disciplines.