Deep Learning Evolutionary Optimization for Regression of Rotorcraft Vibrational Spectra

Martinez, Daniel; Brewer, Wesley; Behm, Gregory; Strelzoff, Andrew; Wilson, Andrew Gordon; Wade, Daniel

doi:10.1109/mlhpc.2018.8638645

Cited by 6 publications

(2 citation statements)

References 9 publications

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“…Leveraging HPC along with supplementary machine learning techniques has been shown to both reduce the time to solution down to hours, as well as increase the performance of the network, often beyond what a domain expert could achieve [18]. There have been a variety of approaches for designing hyperparameters for deep learning on HPC, including using support vector machines to drive prediction of good hyperparameter sets [19], using Bayesian optimization [20], and using genetic algorithms [18], [21]. HPC is very well-suited to addressing this phase of deep learning as determining the appropriate design for the deep learning network often requires evaluating thousands to millions of potential network designs.…”

Section: Deep Learning and High Performance Computingmentioning

confidence: 99%

Exascale Deep Learning to Accelerate Cancer Research

Patton

Abousamra

Samaras

et al. 2019

2019 IEEE International Conference on Big Data (Big Data)

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Deep learning, through the use of neural networks, has demonstrated remarkable ability to automate many routine tasks when presented with sufficient data for training. The neural network architecture (e.g. number of layers, types of layers, connections between layers, etc.) plays a critical role in determining what, if anything, the neural network is able to learn from the training data. The trend for neural network architectures, especially those trained on ImageNet, has been to grow ever deeper and more complex. The result has been ever increasing accuracy on benchmark datasets with the cost of increased computational demands. In this paper we demonstrate that neural network architectures can be automatically generated, tailored for a specific application, with dual objectives: accuracy of prediction and speed of prediction. Using MENNDLan HPC-enabled software stack for neural architecture search-we generate a neural network with comparable accuracy to state-ofthe-art networks on a cancer pathology dataset that is also 16× faster at inference. The speedup in inference is necessary because of the volume and velocity of cancer pathology data; specifically, the previous state-of-the-art networks are too slow for individual researchers without access to HPC systems to keep pace with the rate of data generation. Our new model enables researchers with modest computational resources to analyze newly generated data faster than it is collected.

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Section: Deep Learning and High Performance Computingmentioning

confidence: 99%

Exascale Deep Learning to Accelerate Cancer Research

Patton

Abousamra

Samaras

et al. 2019

2019 IEEE International Conference on Big Data (Big Data)

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“…A data-driven framework was proposed to develop safety-based diagnostics for rotorcrafts and to define the process of selecting a single, airworthy MLbased diagnostic classifier that replaces a suite of fielded condition indicators (CI) [54]. A high-performance parallel computing framework for deep neural network (DNN) hyperparameter search using evolutionary optimization was proposed for nonlinear high-dimensional multivariate regression problems for condition monitoring of rotorcrafts [17]. The developed DNN models were capable of mapping existing CI to helicopter oil cooler vibration spectra and thereby infer the quality of the internal bearing faults [18].…”

Section: Introductionmentioning

confidence: 99%

The stochastic aeroelastic response analysis of helicopter rotors using deep and shallow machine learning

Chatterjee

Essien

Ganguli

et al. 2021

Neural Comput & Applic

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This paper addresses the influence of manufacturing variability of a helicopter rotor blade on its aeroelastic responses. An aeroelastic analysis using finite elements in spatial and temporal domains is used to compute the helicopter rotor frequencies, vibratory hub loads, power required and stability in forward flight. The novelty of the work lies in the application of advanced data-driven machine learning (ML) techniques, such as convolution neural networks (CNN), multi-layer perceptron (MLP), random forests, support vector machines and adaptive Gaussian process (GP) for capturing the nonlinear responses of these complex spatio-temporal models to develop an efficient physics-informed ML framework for stochastic rotor analysis. Thus, the work is of practical significance as (i) it accounts for manufacturing uncertainties, (ii) accurately quantifies their effects on nonlinear response of rotor blade and (iii) makes the computationally expensive simulations viable by the use of ML. A rigorous performance assessment of the aforementioned approaches is presented by demonstrating validation on the training dataset and prediction on the test dataset. The contribution of the study lies in the following findings: (i) The uncertainty in composite material and geometric properties can lead to significant variations in the rotor aeroelastic responses and thereby highlighting that the consideration of manufacturing variability in analyzing helicopter rotors is crucial for assessing their behaviour in real-life scenarios. (ii) Precisely, the substantial effect of uncertainty has been observed on the six vibratory hub loads and the damping with the highest impact on the yawing hub moment. Therefore, sufficient factor of safety should be considered in the design to alleviate the effects of perturbation in the simulation results. (iii) Although advanced ML techniques are harder to train, the optimal model configuration is capable of approximating the nonlinear response trends accurately. GP and CNN followed by MLP achieved satisfactory performance. Excellent accuracy achieved by the above ML techniques demonstrates their potential for application in the optimization of rotors under uncertainty.

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