Multi Physic

Multiphysics modeling aims to simulate systems involving coupled physical phenomena, such as fluid dynamics, heat transfer, and structural mechanics, which are common in many real-world scenarios. Current research heavily utilizes machine learning, particularly physics-informed neural networks (PINNs) and neural operators, often integrated with traditional numerical methods like finite element analysis, to improve accuracy and efficiency in solving complex multiphysics problems. This field is significant because it enables more accurate and computationally tractable simulations across diverse scientific and engineering domains, leading to improved designs, predictions, and ultimately, a deeper understanding of complex systems.