The field of computer graphics and simulation is rapidly evolving, with a focus on achieving real-time performance and high-fidelity results. Recent developments have centered around the use of neural networks and machine learning algorithms to accelerate rendering, simulation, and other computationally intensive tasks. One notable trend is the integration of neural networks with traditional rendering pipelines, enabling faster and more efficient rendering of complex scenes. Additionally, there is a growing interest in using machine learning to simulate real-world phenomena, such as fluid dynamics and acoustic transfer, with high accuracy and speed. These advances have significant implications for a wide range of applications, including virtual reality, video games, and scientific visualization. Noteworthy papers in this area include Lumina, which proposes a hardware-algorithm co-designed system for real-time mobile neural rendering, and TransGI, which introduces a novel neural rendering method for real-time dynamic global illumination. NAT is also noteworthy for its innovative approach to real-time acoustic transfer simulation using implicit neural representations.