Monte Carlo Sampling

Monte Carlo sampling is a computational technique that uses random sampling to obtain numerical results for problems that are difficult to solve deterministically. Current research focuses on improving its efficiency and accuracy, particularly for rare event simulation and high-dimensional problems, employing methods like neural networks to guide sampling, Gaussian processes for uncertainty quantification, and generative models to directly sample from complex distributions. These advancements are impacting diverse fields, from biopharmaceutical manufacturing (improving process monitoring and prediction) to materials science (accelerating simulations of atomic-scale phenomena) and machine learning (enhancing model explainability and robustness).