92 citations · 124 across the 6 of their papers we have counts for
6 papers
Combining Machine Learning with Computational Fluid Dynamics using OpenFOAM and SmartSim
Tomislav Maric, Mohammed Elwardi Fadeli, Alessandro Rigazzi +2
Combining machine learning (ML) with computational fluid dynamics (CFD) opens many possibilities for improving simulations of technical and natural systems. However, CFD+ML algorit…
In Situ Framework for Coupling Simulation and Machine Learning with Application to CFD
Riccardo Balin, Filippo Simini, Cooper Simpson +7
Recent years have seen many successful applications of machine learning (ML) to facilitate fluid dynamic computations. As simulations grow, generating new training datasets for tra…
The Boundary for Quantum Advantage in Gaussian Boson Sampling
Jacob F. F. Bulmer, Bryn A. Bell, Rachel S. Chadwick +9
Identifying the boundary beyond which quantum machines provide a computational advantage over their classical counterparts is a crucial step in charting their usefulness. Gaussian…
Using Machine Learning at Scale in HPC Simulations with SmartSim: An Application to Ocean Climate Modeling
Sam Partee, Matthew Ellis, Alessandro Rigazzi +4
We demonstrate the first climate-scale, numerical ocean simulations improved through distributed, online inference of Deep Neural Networks (DNN) using SmartSim. SmartSim is a libra…
DC-S3GD: Delay-Compensated Stale-Synchronous SGD for Large-Scale Decentralized Neural Network Training
Alessandro Rigazzi
Data parallelism has become the de facto standard for training Deep Neural Network on multiple processing units. In this work we propose DC-S3GD, a decentralized (without Parameter…
Recombination of Artificial Neural Networks
Aaron Vose, Jacob Balma, Alex Heye +5
We propose a genetic algorithm (GA) for hyperparameter optimization of artificial neural networks which includes chromosomal crossover as well as a decoupling of parameters (i.e.,…