PINN application paper

[gyouhoc]

Hi, I publish a paper about the PINN application for Li ion battery temp prediction.
Would you put my paper on your list?

G. Cho, M. Wang, Y. Kim, J. Kwon and W. Su, "A Physics-Informed Machine Learning Approach for Estimating Lithium-Ion Battery Temperature," in IEEE Access, vol. 10, pp. 88117-88126, 2022, doi: 10.1109/ACCESS.2022.3199652.

Thank you

[weipeng0098]

Sure, my pleasure~

[engsbk]

Thank you for the amazing library!
Can you also consider adding these papers to the application section:

[1] S. Alkhadhr and M. Almekkawy, “Wave Equation Modeling via Physics-Informed Neural Networks: Models of Soft and Hard Constraints for Initialand Boundary Conditions,” Sensors, vol. 23, no. 5, p. 2792, Mar. 2023.
[2] S. Alkhadhr, X. Liu, and M. Almekkawy, “Modeling of the Forward Wave Propagation Using Physics-Informed Neural Networks,” in IEEE International Ultrasonics Symposium, IUS, 2021, pp. 1–4.
[3] S. Alkhadhr and M. Almekkawy, “Modeling of the Wave Propagation of a Multi-Element Ultrasound Transducer Using Neural Networks,” in IEEE International Ultrasonics Symposium, IUS, 2021, pp. 1–4.
[4] Y. Wang, S. Alkhadhr, and M. Almekkawy, “PINN Simulation of the Temperature Rise Due to Ultrasound Wave Propagation,” IEEE Int. Ultrason. Symp. IUS, pp. 21–24, 2021.
[5] S. Alkhadhr and M. Almekkawy, “A Combination of Deep Neural Networks and Physics to Solve the Inverse Problem of Burger ’ s Equation,” in IEEE Engineering in Medicine & Biology Society (EMBC), 2021, pp. 4471–4474.
[6] S. Alkhadhr and M. Almekkawy, “Modeling the Wave Equation Using Physics-Informed Neural Networks Enhanced With Attention to Loss Weights,” ICASSP 2023 - 2023 IEEE Int. Conf. Acoust. Speech Signal Process., pp. 1–5, 2023.