The field of energy and transportation systems is witnessing significant developments with the integration of physics-informed models. These models are being used to improve the accuracy and efficiency of various systems, including fluid dynamics, traffic state estimation, and electric vehicle parameter estimation. The use of physics-informed neural networks and deep operator networks is becoming increasingly popular, as they can effectively capture complex physical phenomena and provide reliable predictions. Notably, these models are being applied to real-world problems, such as denoising flow images, estimating traffic states, and optimizing electric vehicle performance. The results show promising improvements in accuracy and robustness, making these models attractive for practical applications. Noteworthy papers include: The paper on fluid dynamics and domain reconstruction from noisy flow images using physics-informed neural networks and quasi-conformal mapping, which demonstrates a robust method for reconstructing high-quality flow images. The paper on a hybrid surrogate for electric vehicle parameter estimation and power consumption via physics-informed neural operators, which presents a novel architecture for estimating vehicle parameters and power consumption.