Search Results for author:
Found 5 papers, 2 papers with code
Combining Machine Learning with Computational Fluid Dynamics using OpenFOAM and SmartSim
Combining machine learning (ML) with computational fluid dynamics (CFD) opens many possibilities for improving simulations of technical and natural systems.
In Situ Framework for Coupling Simulation and Machine Learning with Application to CFD
Recent years have seen many successful applications of machine learning (ML) to facilitate fluid dynamic computations.
Using Machine Learning at Scale in HPC Simulations with SmartSim: An Application to Ocean Climate Modeling
We demonstrate the first climate-scale, numerical ocean simulations improved through distributed, online inference of Deep Neural Networks (DNN) using SmartSim.
DC-S3GD: Delay-Compensated Stale-Synchronous SGD for Large-Scale Decentralized Neural Network Training
Data parallelism has become the de facto standard for training Deep Neural Network on multiple processing units.
Recombination of Artificial Neural Networks
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., weights and biases) from hyperparameters (e. g., learning rate, weight decay, and dropout) during sexual reproduction.
