Graph theory and tools from network science are used to characterize and model the behavior of various fluid dynamics problems and fungal-host interactions.

Selected publications:

• M. Gopalakrishnan Meena and K. Taira, "Identifying vortical network connectors for turbulent flow modification", Journal of Fluid Mechanics, 915, A10, 2021

• M. Gopalakrishnan Meena, M. J. Lane et al., "A glimpse into the fungal metabolomic abyss: Novel network analysis reveals relationships between exogenous compounds and their outputs", PNAS Nexus, Volume 2, Issue 10, 2023

Machine learning techniques for regression and segmentation tasks are used for turbulence modeling and biomedical image decomposition and segmentation.

Selected publications:

• M. Gopalakrishnan Meena et al., "Machine-learned closure of URANS for stably stratified turbulence: connecting physical timescales & data hyperparameters of deep time-series models," Machine Learning: Science and Technology, 5 (4), 045063, 2024

• M. Gopalakrishnan Meena et al., "Physics guided machine learning for multi-material decomposition of tissues from dual-energy CT scans of simulated breast models with calcifications," High Performance Computing for Imaging Conference at Electronic Imaging Symposium, San Francisco, CA, January 15-19, 2023

Quantum computing is used to tackle canonical fluid dynamics problems and other high-performance computing applications.

Selected publications:

• M. Gopalakrishnan Meena et al., "Solving the Hele-Shaw flow using the Harrow-Hassidim-Lloyd algorithm on superconducting devices: A study of efficiency and challenges," Physics of Fluids, 36 (10): 101705, 2024

• T. Beck et al., "Integrating Quantum Computing Resources into Scientific HPC Ecosystems," Future Generation Computer Systems, 161, 11-25, 2024