Virtual Reaction Network

Virtual reaction networks are simplified representations of complex chemical or physical systems, aiming to capture essential dynamics with reduced computational cost. Current research focuses on developing efficient algorithms, such as machine learning-based optimization techniques, to construct and refine these networks, often incorporating techniques like singular value decomposition for dimensionality reduction. These models find applications in diverse fields, including combustion modeling and autonomous navigation, where they enable faster simulations and improved safety by providing high-fidelity approximations of complex processes with fewer computational resources. The development of robust and accurate virtual reaction networks is crucial for advancing our understanding and control of intricate systems across various scientific and engineering disciplines.