4 citations · 5 across the 12 of their papers we have counts for
5 papers · 1 filter
Diff-NekRS: A Scalable Differentiable Framework for Multi-Timestep Solver-in-the-Loop Training
Junoh Jung, Riccardo Balin, Bethany Lusch +1
Hybrid physics-machine-learning solvers improve under-resolved simulations by embedding trainable corrections into the time integration. During autoregressive inference, repeated s…
Super-Resolution Initialization of High-Fidelity CFD Simulations for Pebble-Bed Reactors
Guilherme Gottems, Vasil Ivakimov, Luiz Aldeia Machado +13
High-order CFD simulations provide detailed resolution of the heterogeneous interstitial flow in pebble-bed reactors, but their computational cost is high, especially during the in…
Application Failures and Machine Computational Efficiency
Carlo Graziani, Bethany Lusch, O. E. Bronson Messer
We present a framework for evaluating uptime efficiency of Exascale-class scientific computers when application failure rates are appreciable. This is the situation that confronts…
A Scalable Pattern Mining Workflow for Interpretable Machine Log Analysis in High-Performance Computing Environments
Shilpika Shilpika, Bethany Lusch, Eric Pershey +3
Modern supercomputers housed in High Performance Computing (HPC) environments generate massive volumes of log data daily, revealing intricate information and performance metrics ab…
Probabilistic Attribution For Large Language Models
Shilpika Shilpika, Carlo Graziani, Bethany Lusch +2
The generative nature of Large Language Models (LLMs) is reflected in the conditional probabilities they compute to sample each response token given the previous tokens. These prob…