Phase Field

Phase field modeling is a computational technique used to simulate the evolution of interfaces and microstructures in various materials and systems. Current research emphasizes developing efficient surrogate models, often employing neural networks like U-Nets, convolutional neural networks, and neural operators, to accelerate computationally expensive phase field simulations, particularly for complex systems like concrete fracture or multicomponent alloys. This accelerates materials discovery and design by enabling faster exploration of phase diagrams and microstructure evolution, with applications ranging from predicting material properties to optimizing manufacturing processes. The integration of machine learning techniques is significantly improving the speed and accuracy of these simulations, leading to more efficient scientific discovery.