most citedIntelligent Sampling of Extreme-Scale Turbulence Datasets for Accurate and Efficient Spatiotemporal Model Training

3 citations · 5 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2025★ 3 cited

Intelligent Sampling of Extreme-Scale Turbulence Datasets for Accurate and Efficient Spatiotemporal Model Training

Wesley Brewer, Murali Meena Gopalakrishnan, Matthias Maiterth +12

With the end of Moore's law and Dennard scaling, efficient training increasingly requires rethinking data volume. Can we train better models with significantly less data via intell…

cs.DC2025

Scaling the memory wall using mixed-precision -- HPG-MxP on an exascale machine

Aditya Kashi, Nicholson Koukpaizan, Hao Lu +3

Mixed-precision algorithms have been proposed as a way for scientific computing to benefit from some of the gains seen for artificial intelligence (AI) on recent high performance c…

cs.CE2024★ 1 cited

Mixed-precision numerics in scientific applications: survey and perspectives

Aditya Kashi, Hao Lu, Wesley Brewer +4

The explosive demand for artificial intelligence (AI) workloads has led to a significant increase in silicon area dedicated to lower-precision computations on recent high-performan…

cs.LG2024

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

Aditya Kashi, Arka Daw, Muralikrishnan Gopalakrishnan Meena +1

Neural operators such as the Fourier Neural Operator (FNO) have been shown to provide resolution-independent deep learning models that can learn mappings between function spaces. F…

cs.LG2024

Scalable Artificial Intelligence for Science: Perspectives, Methods and Exemplars

Wesley Brewer, Aditya Kashi, Sajal Dash +4

In a post-ChatGPT world, this paper explores the potential of leveraging scalable artificial intelligence for scientific discovery. We propose that scaling up artificial intelligen…

physics.flu-dyn2024★ 1 cited

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 +4

We develop time-series machine learning (ML) methods for closure modeling of the Unsteady Reynolds Averaged Navier Stokes (URANS) equations applied to stably stratified turbulence…