Pence, I. Et Al. 2023. Evaluation of machine learning approaches for estimating thermodynamic properties of new generation refrigerant R513A. Sustainable Energy Technologies and Assessments , vol.55 .

Pence, I., Yıldırım, R., Siseci Cesmeli, M., GÜNGÖR, A., & Akyüz, A., (2023). Evaluation of machine learning approaches for estimating thermodynamic properties of new generation refrigerant R513A. Sustainable Energy Technologies and Assessments , vol.55.

Pence, I. Et Al. "Evaluation of machine learning approaches for estimating thermodynamic properties of new generation refrigerant R513A," Sustainable Energy Technologies and Assessments , vol.55, 2023

Pence, I. Et Al. "Evaluation of machine learning approaches for estimating thermodynamic properties of new generation refrigerant R513A." Sustainable Energy Technologies and Assessments , vol.55, 2023

Pence, I. Et Al. (2023) . "Evaluation of machine learning approaches for estimating thermodynamic properties of new generation refrigerant R513A." Sustainable Energy Technologies and Assessments , vol.55.

@article{article, author={I. Pence Et Al. }, title={Evaluation of machine learning approaches for estimating thermodynamic properties of new generation refrigerant R513A}, journal={Sustainable Energy Technologies and Assessments}, year=2023}