Randomized methods to characterize large-scale vortical flow network. - Zhe Bai; N. Benjamin Erichson; Muralikrishnan Gopalakrishnan Meena; Kunihiko Taira; Steven L. Brunton (CoRR, 2019)

Network community-based analysis of complex vortical flows: Laminar and turbulent flows - Muralikrishnan Gopalakrishnan Meena (2020)

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 004 (High Performance Computing for Imaging, 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 (SC Workshops, 2023)

Towards a Quantum Algorithm for the Incompressible Nonlinear Navier-Stokes Equations. - Muralikrishnan Gopalakrishnan Meena; Yu Zhang; Weiwen Jiang; Youzuo Lin; Stefanie Günther; Xinfeng Gao (QCE, 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)

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 001 (CoRR, 2024)

Integrating quantum computing resources into scientific HPC ecosystems. - Thomas Beck; Alessandro Baroni 003; Ryan S. Bennink; Gilles Buchs; Eduardo Antonio Coello Pérez; Markus Eisenbach 002; Rafael Ferreira da Silva; Muralikrishnan Gopalakrishnan Meena; Kalyana C. Gottiparthi; Peter Groszkowski; Travis S. Humble; Ryan Landfield; et al. (Future Gener. Comput. Syst., 2024)

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 004; Bennett A. Landman; Yuankai Huo (CoRR, 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, 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, 2025)

Intelligent Sampling of Extreme-Scale Turbulence Datasets for Accurate and Efficient Spatiotemporal Model Training. - Wesley Brewer; Muralikrishnan Gopalakrishnan Meena; Matthias Maiterth; Aditya Kashi; Jong Youl Choi; Pei Zhang; Stephen Nichols; Riccardo Balin; Miles Couchman; Stephen M. de Bruyn Kops; P. K. Yeung; Daniel Dotson; et al. (CoRR, 2025)

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)