The field of simulation and prediction is witnessing a significant shift towards the use of neural networks and physics-informed models. These innovative approaches are enabling researchers to tackle complex problems in various domains, including human-computer interaction, ocean pollutant modeling, turbulent fluid flows, and hair simulation. The use of neural networks is allowing for the creation of more realistic and adaptive models, while physics-informed models are providing a means to incorporate physical laws and constraints into the modeling process. This is leading to more accurate and reliable predictions, as well as the ability to simulate complex phenomena that were previously difficult or impossible to model. Notable papers in this area include NeuralOS, which simulates graphical user interfaces using neural generative models, and Physics-Informed Neural Networks for modeling ocean pollutant, which uses a PINN framework to simulate the dispersion of pollutants. Additionally, papers such as Simulating Three-dimensional Turbulence with Physics-informed Neural Networks and HairFormer: Transformer-Based Dynamic Neural Hair Simulation are pushing the boundaries of what is possible in their respective fields.