Complex Physical System

Research on complex physical systems focuses on developing efficient and accurate computational models, often leveraging machine learning to overcome limitations of traditional numerical methods like finite element analysis. Current efforts center on integrating physical laws and constraints into neural network architectures (e.g., Physics-Informed Neural Networks, Graph Neural Networks, and operator networks) to improve accuracy and generalization, particularly for systems with complex geometries and multi-physics interactions. This work is significant because it promises faster, more accurate simulations across diverse scientific and engineering domains, enabling improved design, control, and predictive maintenance of complex systems.