Multi Physic Simulation

Multi-physics simulation aims to accurately model systems involving multiple interacting physical phenomena, enabling more realistic and comprehensive predictions. Current research focuses on developing efficient and scalable simulation methods, such as differentiable lattice Boltzmann methods and hybrid analytical-numerical models, often integrated with machine learning techniques like reinforcement learning and physics-informed neural networks to improve accuracy, speed, and optimization capabilities. This field is crucial for advancing scientific understanding across diverse domains and facilitating the design and optimization of complex engineering systems, particularly through the development of digital twins and surrogate models for efficient exploration of design spaces.