29,595 research outputs found
Evaluation of machine learning architectures on the quantification of epistemic and aleatoric uncertainties in complex dynamical systems
Machine learning methods for the construction of data-driven reduced order
model models are used in an increasing variety of engineering domains,
especially as a supplement to expensive computational fluid dynamics for design
problems. An important check on the reliability of surrogate models is
Uncertainty Quantification (UQ), a self assessed estimate of the model error.
Accurate UQ allows for cost savings by reducing both the required size of
training data sets and the required safety factors, while poor UQ prevents
users from confidently relying on model predictions. We examine several machine
learning techniques, including both Gaussian processes and a family
UQ-augmented neural networks: Ensemble neural networks (ENN), Bayesian neural
networks (BNN), Dropout neural networks (D-NN), and Gaussian neural networks
(G-NN). We evaluate UQ accuracy (distinct from model accuracy) using two
metrics: the distribution of normalized residuals on validation data, and the
distribution of estimated uncertainties. We apply these metrics to two model
data sets, representative of complex dynamical systems: an ocean engineering
problem in which a ship traverses irregular wave episodes, and a dispersive
wave turbulence system with extreme events, the Majda-McLaughlin-Tabak model.
We present conclusions concerning model architecture and hyperparameter tuning.Comment: Submitted for publication to "Computer Methods in Applied Mechanics
and Engineering." 25 pages, 20 figures. arXiv admin note: text overlap with
arXiv:1505.05424 by other author
The use of artificial neural networks to retrieve sea-level information from remote data sources
The knowledge of near-shore sea-level variations is of great importance in applications such as ocean engineering and safe navigation. It also plays an essential role in the practical realisation of the height reference surface in geodesy. In the cases of gaps in tide-gauge records, estimates can be obtained by various methods of interpolation and/or extrapolation, which generally assume linearity of the data. Although plausible in many cases, this assumption does not provide accurate results because shallow-water oceanic processes, such as tides, are mostly of a non-linear nature. This paper employs artificial neural networks to supplement hourly tide-gauge records using observations from other distant tide gauges. A case study is presented using data from the SEAFRAME tide-gauge sta-tions at Hillarys Boat Harbour, Indian Ocean, and Esperance, Southern Ocean, for the period 1992 to 2002. The neural network methodology of sea-level supplementation demonstrates reliable results, with a fairly good overall agreement between the retrieved information and actual measurements
Characterizing Evaporation Ducts Within the Marine Atmospheric Boundary Layer Using Artificial Neural Networks
We apply a multilayer perceptron machine learning (ML) regression approach to
infer electromagnetic (EM) duct heights within the marine atmospheric boundary
layer (MABL) using sparsely sampled EM propagation data obtained within a
bistatic context. This paper explains the rationale behind the selection of the
ML network architecture, along with other model hyperparameters, in an effort
to demystify the process of arriving at a useful ML model. The resulting speed
of our ML predictions of EM duct heights, using sparse data measurements within
MABL, indicates the suitability of the proposed method for real-time
applications.Comment: 13 pages, 7 figure
Gaussian Process Regression for Estimating EM Ducting Within the Marine Atmospheric Boundary Layer
We show that Gaussian process regression (GPR) can be used to infer the
electromagnetic (EM) duct height within the marine atmospheric boundary layer
(MABL) from sparsely sampled propagation factors within the context of bistatic
radars. We use GPR to calculate the posterior predictive distribution on the
labels (i.e. duct height) from both noise-free and noise-contaminated array of
propagation factors. For duct height inference from noise-contaminated
propagation factors, we compare a naive approach, utilizing one random sample
from the input distribution (i.e. disregarding the input noise), with an
inverse-variance weighted approach, utilizing a few random samples to estimate
the true predictive distribution. The resulting posterior predictive
distributions from these two approaches are compared to a "ground truth"
distribution, which is approximated using a large number of Monte-Carlo
samples. The ability of GPR to yield accurate and fast duct height predictions
using a few training examples indicates the suitability of the proposed method
for real-time applications.Comment: 15 pages, 6 figure
Unifying Multiple Knowledge Domains Using the ARTMAP Information Fusion System
Sensors working at different times, locations, and scales, and experts with different goals, languages, and situations, may produce apparently inconsistent image labels that are reconciled by their implicit underlying relationships. Even when such relationships are unknown to the user, an ARTMAP information fusion system discovers a hierarchical knowledge structure for a labeled dataset. The present paper addresses the problem of integrating two or more independent knowledge hierarchies based on the same low-level classes. The new system fuses independent domains into a unified knowledge structure, discovering cross-domain rules in this process. The system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships. In order to self-organize its expert system, ARTMAP information fusion system features distributed code representations that exploit the neural network’s capacity for one-to-many learning. The fusion system software and testbed datasets are available from http://cns.bu.edu/techlabNational Science Foundation (SBE-0354378); National Geospatial-Intelligence Agency (NMA 201-01-1-2016
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