基于图神经网络的电动汽车充电负荷预测与需求响应优化

The integration of physics-based modelling and artificial intelligence (AI) is transforming semiconductor device simulation, facilitating unparalleled precision, efficiency, and predictive power. Conventional semiconductor modelling is based on first-principles physics, including drift-diffusion equations, Boltzmann transport models, and quantum mechanical methods. Nonetheless, these methods frequently encounter computational constraints when tackling intricate nanoscale processes. Novel AI-driven approaches, including as deep learning, physics-informed neural networks (PINNs), and alternative modelling, provide innovative ways to address these difficulties. The article explores recent progress in the integration of AI with semiconductor device physics, highlighting hybrid methodologies that preserve physical interpretability while utilising data-driven insights. In response to these improvements, machine learning-assisted compact modelling (MLCM) has garnered considerable attention as an alternative to conventional white-box modelling. These opaque methodologies seek to deliver versatile modelling for intricate mathematical and physical events through the training of neural networks using empirical and simulated data. This facilitates the creation of a precise closed-form correlation between output attributes and input parameters associated with the fabrication process and device functionality. Primary applications encompass swift process optimisation, concise model formulation, and inverse design for advanced electronics. It addresses the benefits and constraints of AI-based modelling, emphasising prospective approaches for combining physics-driven and data-driven paradigms. This interdisciplinary synthesis aims to expedite semiconductor research and development, promoting more efficient and scalable device design methodologies.