227 research outputs found
Neuromorphic computing using wavelength-division multiplexing
Optical neural networks (ONNs), or optical neuromorphic hardware
accelerators, have the potential to dramatically enhance the computing power
and energy efficiency of mainstream electronic processors, due to their
ultralarge bandwidths of up to 10s of terahertz together with their analog
architecture that avoids the need for reading and writing data back and forth.
Different multiplexing techniques have been employed to demonstrate ONNs,
amongst which wavelength division multiplexing (WDM) techniques make sufficient
use of the unique advantages of optics in terms of broad bandwidths. Here, we
review recent advances in WDM based ONNs, focusing on methods that use
integrated microcombs to implement ONNs. We present results for human image
processing using an optical convolution accelerator operating at 11 Tera
operations per second. The open challenges and limitations of ONNs that need to
be addressed for future applications are also discussed.Comment: 13 pages, 8 figures, 160 reference
Towards Automated Metamorphic Test Identification for Ocean System Models
Metamorphic testing seeks to verify software in the absence of test oracles.
Our application domain is ocean system modeling, where test oracles rarely
exist, but where symmetries of the simulated physical systems are known. The
input data set is large owing to the requirements of the application domain.
This paper presents work in progress for the automated generation of
metamorphic test scenarios using machine learning. We extended our previously
proposed method [1] to identify metamorphic relations with reduced
computational complexity. Initially, we represent metamorphic relations as
identity maps. We construct a cost function that minimizes for identifying a
metamorphic relation orthogonal to previously found metamorphic relations and
penalize for the identity map. A machine learning algorithm is used to identify
all possible metamorphic relations minimizing the defined cost function. We
propose applying dimensionality reduction techniques to identify attributes in
the input which have high variance among the identified metamorphic relations.
We apply mutation on these selected attributes to identify distinct metamorphic
relations with reduced computational complexity. For experimental evaluation,
we subject the two implementations of an ocean-modeling application to the
proposed method to present the use of metamorphic relations to test the two
implementations of this application.Comment: 5 Pages, 1 Figur
Causal networks for climate model evaluation and constrained projections
Global climate models are central tools for understanding past and future climate change. The assessment of model skill, in turn, can benefit from modern data science approaches. Here we apply causal discovery algorithms to sea level pressure data from a large set of climate model simulations and, as a proxy for observations, meteorological reanalyses. We demonstrate how the resulting causal networks (fingerprints) offer an objective pathway for process-oriented model evaluation. Models with fingerprints closer to observations better reproduce important precipitation patterns over highly populated areas such as the Indian subcontinent, Africa, East Asia, Europe and North America. We further identify expected model interdependencies due to shared development backgrounds. Finally, our network metrics provide stronger relationships for constraining precipitation projections under climate change as compared to traditional evaluation metrics for storm tracks or precipitation itself. Such emergent relationships highlight the potential of causal networks to constrain longstanding uncertainties in climate change projections. Algorithms to assess causal relationships in data sets have seen increasing applications in climate science in recent years. Here, the authors show that these techniques can help to systematically evaluate the performance of climate models and, as a result, to constrain uncertainties in future climate change projections
Reduced order modeling of fluid flows: Machine learning, Kolmogorov barrier, closure modeling, and partitioning
In this paper, we put forth a long short-term memory (LSTM) nudging framework
for the enhancement of reduced order models (ROMs) of fluid flows utilizing
noisy measurements. We build on the fact that in a realistic application, there
are uncertainties in initial conditions, boundary conditions, model parameters,
and/or field measurements. Moreover, conventional nonlinear ROMs based on
Galerkin projection (GROMs) suffer from imperfection and solution instabilities
due to the modal truncation, especially for advection-dominated flows with slow
decay in the Kolmogorov width. In the presented LSTM-Nudge approach, we fuse
forecasts from a combination of imperfect GROM and uncertain state estimates,
with sparse Eulerian sensor measurements to provide more reliable predictions
in a dynamical data assimilation framework. We illustrate the idea with the
viscous Burgers problem, as a benchmark test bed with quadratic nonlinearity
and Laplacian dissipation. We investigate the effects of measurements noise and
state estimate uncertainty on the performance of the LSTM-Nudge behavior. We
also demonstrate that it can sufficiently handle different levels of temporal
and spatial measurement sparsity. This first step in our assessment of the
proposed model shows that the LSTM nudging could represent a viable realtime
predictive tool in emerging digital twin systems
Spatial Data Analysis Utilizing Grid Dbscan Algorithm in Clustering Techniques for Partial Object Classification Issues
Clustering algorithms to solve problems with partial object categorization in spatial data analysis is the topic of this research, which explores the usefulness of these techniques. In order to do this, the Grid-DBSCAN method is offered as an effective clustering tool for the purpose of resolving issues involving partial object categorization. A grid-based technique is included into the Grid-DBSCAN algorithm, which is derived from the DBSCAN algorithm and is designed to increase its overall performance. A number of datasets taken from the real world are used to evaluate the method, and it is then compared to existing clustering techniques. The findings of the experiments indicate that the Grid-DBSCAN method is superior to the other clustering algorithms in terms of accuracy and resilience, and that it is able to locate the most effective solution for jobs involving partial object categorization. It is also possible to enhance the Grid-DBSCAN technique so that it can handle different kinds of complicated datasets. The purpose of this study is to offer an understanding of the efficiency of the suggested method and its potential to perform partial object categorization problems in spatial data analysis
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