Numerical Integration

Numerical integration, the approximation of definite integrals, is crucial for solving numerous problems across scientific disciplines. Current research emphasizes developing efficient and accurate methods, particularly for high-dimensional data and complex systems, focusing on techniques like cubature and sampling-based approaches, as well as leveraging neural networks and adaptive algorithms to improve computational speed and accuracy. These advancements are impacting diverse fields, from reinforcement learning in robotics to multi-omics data analysis and the simulation of complex physical systems, enabling more sophisticated modeling and analysis.