Serveh Kamrava

Assistant Professor

Dr. Serveh Kamrava’s research is focused on using machine learning and multiscale modeling methods for solving problems related to porous media focused. The goal is to develop physics-guided machine learning methods that can integrate physical laws and available data at small- and large-scale systems. Dr. Kamrava’s group uses machine learning to discover physics when large data is available and the physical laws are not well known. The efficient ways of finding patterns in big data by integrating known physics into data-driven methods are also explored which requires using both machine learning methods and numerical simulations. 

Education

• PhD, University of Southern California, Chemical Engineering
• MS, Texas A&M University, Chemical Engineering
• BS, Sahand University of Technology, Chemical Engineering

Research Interests

• Machine Learning
• Fluid Dynamics
• Chemical Discovery
• Energy Storage Systems
• Complex Materials

Selected Publications

• Kamrava, S., Im, J., De Barros, F., & Sahimi, M. (2021) Estimating Dispersion Coefficient in Flow Through Heterogeneous Porous Media by a Deep Convolutional Neural Network. Geophysical Research Letters, doi.org/10.1029/2021GL094443
• Kamrava, S., Sahimi, M., & Tahmasebi, P. (2021). Simulating fluid flow in complex porous materials: Integrating the governing equations with deep-layered machines. Nature Computational Materials 7(1), 1-9. doi.org/10.1038/s41524-021-00598-2
• Kamrava, S., Sahimi, M., & Tahmasebi, P. (2021). Physics- and image-based prediction of fluid flow in complex porous membranes and materials by deep learning. Journal of Membrane Science, 119050. doi.org/10.1016/j.memsci.2021.119050
• Tahmasebi, P., Kamrava, S., Bai, T., & Sahimi, M. (2020). Machine Learning in Geo-and Environmental Sciences: From Small to Large Scale. Advances in Water Resources, 103619. doi.org/10.1016/j.advwatres.2020.103619
• Kamrava, S., Sahimi, M., & Tahmasebi, P. (2020). Quantifying accuracy of stochastic methods of reconstructing complex materials by deep learning. Physical Review E, 101(4), 043301. doi.org/10.1103/PhysRevE.101.043301
• Kamrava, S., Tahmasebi, P., & Sahimi, M. (2019). Linking morphology of porous media to their macroscopic permeability by deep learning. Transport in Porous Media: 1-22. doi.org/10.1007/s11242-019-01352-5
• Kamrava, S., Tahmasebi, P., & Sahimi, M. (2019). Enhancing images of shale formations by a hybrid stochastic and deep learning algorithm. Neural Networks 118: 310-320. doi.org/10.1016/j.neunet.2019.07.009

Honors and Awards

• 2022 NSF Award
• 2022 ACS-PRF DNI Award