The field of physics-informed neural networks is rapidly advancing, with a focus on developing innovative models that can accurately capture complex physical processes. Recent developments have seen the introduction of new architectures and techniques, such as the use of adaptive loss functions and conservative constraints, which have improved the performance and reliability of these models. These advancements have significant implications for a range of applications, including the simulation of reactive materials, combustion, and vehicle collision dynamics. Notable papers in this area include: The paper on AMORE, which proposes an adaptive multi-output operator network for stiff chemical kinetics, demonstrating its efficacy in predicting thermochemical states. The paper on solving the BGK model and Boltzmann equation by Fourier Neural Operator with conservative constraints, which efficiently captures the mapping between distribution functions while enforcing physical consistency.