13,432 research outputs found
A self-organising mixture network for density modelling
A completely unsupervised mixture distribution network, namely the self-organising mixture network, is proposed for learning arbitrary density functions. The algorithm minimises the Kullback-Leibler information by means of stochastic approximation methods. The density functions are modelled as mixtures of parametric distributions such as Gaussian and Cauchy. The first layer of the network is similar to the Kohonen's self-organising map (SOM), but with the parameters of the class conditional densities as the learning weights. The winning mechanism is based on maximum posterior probability, and the updating of weights can be limited to a small neighbourhood around the winner. The second layer accumulates the responses of these local nodes, weighted by the learning mixing parameters. The network possesses simple structure and computation, yet yields fast and robust convergence. Experimental results are also presente
Semi-supervised model-based clustering with controlled clusters leakage
In this paper, we focus on finding clusters in partially categorized data
sets. We propose a semi-supervised version of Gaussian mixture model, called
C3L, which retrieves natural subgroups of given categories. In contrast to
other semi-supervised models, C3L is parametrized by user-defined leakage
level, which controls maximal inconsistency between initial categorization and
resulting clustering. Our method can be implemented as a module in practical
expert systems to detect clusters, which combine expert knowledge with true
distribution of data. Moreover, it can be used for improving the results of
less flexible clustering techniques, such as projection pursuit clustering. The
paper presents extensive theoretical analysis of the model and fast algorithm
for its efficient optimization. Experimental results show that C3L finds high
quality clustering model, which can be applied in discovering meaningful groups
in partially classified data
Machine learning for crystal identification and discovery
As computers get faster, researchers -- not hardware or algorithms -- become
the bottleneck in scientific discovery. Computational study of colloidal
self-assembly is one area that is keenly affected: even after computers
generate massive amounts of raw data, performing an exhaustive search to
determine what (if any) ordered structures occur in a large parameter space of
many simulations can be excruciating. We demonstrate how machine learning can
be applied to discover interesting areas of parameter space in colloidal self
assembly. We create numerical fingerprints -- inspired by bond orientational
order diagrams -- of structures found in self-assembly studies and use these
descriptors to both find interesting regions in a phase diagram and identify
characteristic local environments in simulations in an automated manner for
simple and complex crystal structures. Utilizing these methods allows analysis
methods to keep up with the data generation ability of modern high-throughput
computing environments.Comment: Fixed typo, added missing acknowledgment, added supplementary
informatio
An unsupervised machine-learning-based shock sensor for high-order supersonic flow solvers
We present a novel unsupervised machine-learning sock sensor based on
Gaussian Mixture Models (GMMs). The proposed GMM sensor demonstrates remarkable
accuracy in detecting shocks and is robust across diverse test cases with
significantly less parameter tuning than other options. We compare the
GMM-based sensor with state-of-the-art alternatives. All methods are integrated
into a high-order compressible discontinuous Galerkin solver, where two
stabilization approaches are coupled to the sensor to provide examples of
possible applications. The Sedov blast and double Mach reflection cases
demonstrate that our proposed sensor can enhance hybrid sub-cell
flux-differencing formulations by providing accurate information of the nodes
that require low-order blending. Besides, supersonic test cases including high
Reynolds numbers showcase the sensor performance when used to introduce
entropy-stable artificial viscosity to capture shocks, demonstrating the same
effectiveness as fine-tuned state-of-the-art sensors. The adaptive nature and
ability to function without extensive training datasets make this GMM-based
sensor suitable for complex geometries and varied flow configurations. Our
study reveals the potential of unsupervised machine-learning methods,
exemplified by this GMM sensor, to improve the robustness and efficiency of
advanced CFD codes
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