Process Simulation

Process simulation aims to model and analyze complex systems, predicting their behavior under various conditions to optimize performance and understand potential risks. Current research emphasizes integrating machine learning, particularly neural networks and large language models, with physics-based models and traditional simulation techniques to improve accuracy, efficiency, and usability, including automating parameter optimization and uncertainty quantification. This interdisciplinary approach is impacting diverse fields, from engineering design and process optimization to the development of novel computing architectures like neuromorphic engines, by enabling faster, more accurate, and more insightful simulations.