EVENT DETECTION IN MULTI-VARIATE SCIENTIFIC SIMULATIONS USING FEATURE ANOMALY METRICS
Abstract not provided.
Publications
Search results
Jump to search filtersMachine Learning Models of Errors in LES Predictions of Surface Pressure Fluctuations
Abstract not provided.
Convolutional Neural Networks for Frequency Response Predictions
Abstract not provided.
Investigating Turbulent Wall Pressure Fluctuations using Machine Learning Techniques
Abstract not provided.
Using Feature Importance Metrics to Detect Events of Interest in Scientific Computing Applications
Abstract not provided.
Machine learning models of errors in large eddy simulation predictions of surface pressure fluctuations
47th AIAA Fluid Dynamics Conference, 2017
We investigate a novel application of deep neural networks to modeling of errors in prediction of surface pressure fluctuations beneath a compressible, turbulent flow. In this context, the truth solution is given by Direct Numerical Simulation (DNS) data, while the predictive model is a wall-modeled Large Eddy Simulation (LES). The neural network provides a means to map relevant statistical flow-features within the LES solution to errors in prediction of wall pressure spectra. We simulate a number of flat plate turbulent boundary layers using both DNS and wall-modeled LES to build up a database with which to train the neural network. We then apply machine learning techniques to develop an optimized neural network model for the error in terms of relevant flow features.
Development of Machine Learning Models for Turbulent Wall Pressure Fluctuations
Abstract not provided.
Development of Machine Learning Models for Turbulent Wall Pressure Fluctuations
Abstract not provided.
Development of Machine Learning Models for Turbulent Wall Pressure Fluctuations
Abstract not provided.
9 Results