Muralikrishnan Gopalakrishnan Meena

According to our database, Muralikrishnan Gopalakrishnan Meena authored at least 12 papers between 2019 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Bridging paradigms: Designing for HPC-Quantum convergence.

Amir Shehata, Peter Groszkowski, Thomas J. Naughton, Muralikrishnan Gopalakrishnan Meena, Elaine Wong, Daniel Claudino, Rafael Ferreira da Silva, Thomas Beck

Future Gener. Comput. Syst., 2026

2025

Pixel-Resolved Long-Context Learning for Turbulence at Exascale: Resolving Small-scale Eddies Toward the Viscous Limit.

Junqi Yin, Mijanur Palash, M. Paul Laiu, Muralikrishnan Gopalakrishnan Meena, John Gounley, Stephen M. de Bruyn Kops, Feiyi Wang, Ramanan Sankaran, Pei Zhang

CoRR, July, 2025

Building a Software Stack for Quantum-HPC Integration.

Amir Shehata, Peter Groszkowski, Thomas J. Naughton, Muralikrishnan Gopalakrishnan Meena, Elaine Wong, Daniel Claudino, Rafael Ferreira da Silva, Thomas Beck

CoRR, March, 2025

Scale-up Unlearnable Examples Learning with High-Performance Computing.

Yanfan Zhu, Isaac Lyngaas, Muralikrishnan Gopalakrishnan Meena, Mary Ellen Koran, Bradley A. Malin, Daniel Moyer, Shunxing Bao, Anuj J. Kapadia, Xiao Wang, Bennett A. Landman, Yuankai Huo

CoRR, January, 2025

2024

Integrating quantum computing resources into scientific HPC ecosystems.

Thomas Beck, Alessandro Baroni, Ryan S. Bennink, Gilles Buchs, Eduardo Antonio Coello Pérez, Markus Eisenbach, Rafael Ferreira da Silva, Muralikrishnan Gopalakrishnan Meena, Kalyana C. Gottiparthi, Peter Groszkowski, Travis S. Humble, Ryan Landfield, Ketan Maheshwari, Sarp Oral, Michael A. Sandoval, Amir Shehata, In-Saeng Suh, Christopher Zimmer

Future Gener. Comput. Syst., 2024

Learning the boundary-to-domain mapping using Lifting Product Fourier Neural Operators for partial differential equations.

Aditya Kashi, Arka Daw, Muralikrishnan Gopalakrishnan Meena, Hao Lu

CoRR, 2024

Machine-Learned Closure of URANS for Stably Stratified Turbulence: Connecting Physical Timescales & Data Hyperparameters of Deep Time-Series Models.

Muralikrishnan Gopalakrishnan Meena, Demetri Liousas, Andrew D. Simin, Aditya Kashi, Wesley Brewer, James J. Riley, Stephen M. de Bruyn Kops

CoRR, 2024

Towards a Quantum Algorithm for the Incompressible Nonlinear Navier-Stokes Equations.

Muralikrishnan Gopalakrishnan Meena, Yu Zhang, Weiwen Jiang, Youzuo Lin, Stefanie Günther, Xinfeng Gao

Proceedings of the IEEE International Conference on Quantum Computing and Engineering, 2024

2023

Entropy-driven Optimal Sub-sampling of Fluid Dynamics for Developing Machine-learned Surrogates.

Wesley Brewer, Daniel Martínez, Muralikrishnan Gopalakrishnan Meena, Aditya Kashi, Katarzyna Borowiec, Siyan Liu, Christopher Pilmaier, Greg W. Burgreen, Shanti Bhushan

Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, 2023

Physics guided machine learning for multi-material decomposition of tissues from dual-energy CT scans of simulated breast models with calcifications.

Muralikrishnan Gopalakrishnan Meena, Amirkoushyar Ziabari, Singanallur V. Venkatakrishnan, Isaac Lyngaas, Matthew R. Norman, Bálint Joó, Thomas L. Beck, Charles A. Bouman, Anuj J. Kapadia, Xiao Wang

Proceedings of the High Performance Computing for Imaging 2023, 2023

2020

Network community-based analysis of complex vortical flows: Laminar and turbulent flows

Muralikrishnan Gopalakrishnan Meena

PhD thesis, 2020

2019

Randomized methods to characterize large-scale vortical flow network.

Zhe Bai, N. Benjamin Erichson, Muralikrishnan Gopalakrishnan Meena, Kunihiko Taira, Steven L. Brunton

CoRR, 2019