11 skills found
geodynamics / AxisemAxiSEM is a parallel spectral-element method to solve 3D wave propagation in a sphere with axisymmetric or spherically symmetric visco-elastic, acoustic, anisotropic structures.
sayantann11 / Clustering Modelsfor MLlustering in Machine Learning Introduction to Clustering It is basically a type of unsupervised learning method . An unsupervised learning method is a method in which we draw references from datasets consisting of input data without labelled responses. Generally, it is used as a process to find meaningful structure, explanatory underlying processes, generative features, and groupings inherent in a set of examples. Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group and dissimilar to the data points in other groups. It is basically a collection of objects on the basis of similarity and dissimilarity between them. For ex– The data points in the graph below clustered together can be classified into one single group. We can distinguish the clusters, and we can identify that there are 3 clusters in the below picture. It is not necessary for clusters to be a spherical. Such as : DBSCAN: Density-based Spatial Clustering of Applications with Noise These data points are clustered by using the basic concept that the data point lies within the given constraint from the cluster centre. Various distance methods and techniques are used for calculation of the outliers. Why Clustering ? Clustering is very much important as it determines the intrinsic grouping among the unlabeled data present. There are no criteria for a good clustering. It depends on the user, what is the criteria they may use which satisfy their need. For instance, we could be interested in finding representatives for homogeneous groups (data reduction), in finding “natural clusters” and describe their unknown properties (“natural” data types), in finding useful and suitable groupings (“useful” data classes) or in finding unusual data objects (outlier detection). This algorithm must make some assumptions which constitute the similarity of points and each assumption make different and equally valid clusters. Clustering Methods : Density-Based Methods : These methods consider the clusters as the dense region having some similarity and different from the lower dense region of the space. These methods have good accuracy and ability to merge two clusters.Example DBSCAN (Density-Based Spatial Clustering of Applications with Noise) , OPTICS (Ordering Points to Identify Clustering Structure) etc. Hierarchical Based Methods : The clusters formed in this method forms a tree-type structure based on the hierarchy. New clusters are formed using the previously formed one. It is divided into two category Agglomerative (bottom up approach) Divisive (top down approach) examples CURE (Clustering Using Representatives), BIRCH (Balanced Iterative Reducing Clustering and using Hierarchies) etc. Partitioning Methods : These methods partition the objects into k clusters and each partition forms one cluster. This method is used to optimize an objective criterion similarity function such as when the distance is a major parameter example K-means, CLARANS (Clustering Large Applications based upon Randomized Search) etc. Grid-based Methods : In this method the data space is formulated into a finite number of cells that form a grid-like structure. All the clustering operation done on these grids are fast and independent of the number of data objects example STING (Statistical Information Grid), wave cluster, CLIQUE (CLustering In Quest) etc. Clustering Algorithms : K-means clustering algorithm – It is the simplest unsupervised learning algorithm that solves clustering problem.K-means algorithm partition n observations into k clusters where each observation belongs to the cluster with the nearest mean serving as a prototype of the cluster . Applications of Clustering in different fields Marketing : It can be used to characterize & discover customer segments for marketing purposes. Biology : It can be used for classification among different species of plants and animals. Libraries : It is used in clustering different books on the basis of topics and information. Insurance : It is used to acknowledge the customers, their policies and identifying the frauds. City Planning: It is used to make groups of houses and to study their values based on their geographical locations and other factors present. Earthquake studies: By learning the earthquake-affected areas we can determine the dangerous zones. References : Wiki Hierarchical clustering Ijarcs matteucc analyticsvidhya knowm
HoBeZwe / SphericalScattering.jlThis package provides semi-analytical solutions to the scattering of time harmonic and static electromagnetic fields from spherical objects.
Yuhang-Chen-TWC / DSE SSE Demodemo code for our TWC paper, Hybrid Spherical- and Planar-Wave Channel Modeling and Estimation for Terahertz Integrated UM-MIMO and IRS Systems
UT-GlobalSeismology / AnisotimeSoftware for computing travel times of seismic waves in a spherically symmetric, transversely isotropic (TI) medium.
babinkin / Hydrodynamic Problems On MatlabFree falling sphere, Wing in wind tunnel, Ballistics of a spherical projectile,Flight part of Glider and a Rocket, Rolling up of the trailing vortex sheet behind a finite wing, Inverse Method: Superposition of elementary flows and a Radial flow, Von Karman Method, Inverse Method II: Conformal Mapping, Solving Elliptic Partial Differential Equations, Potential Flows, Small Amplitude Wave, A Finite Amplitude Wave Flow in a Elastic Tube, Viscous Fluid Flows, Self-Similar Laminar Flows, Flat-Plate Thermometer and etc.
hbartle / Spherical Vector Wave ExpansionImplementation of the Spherical (Vector) Wave Expansion (SWE) following FEKO definition
alejandroc137 / ScalarWaveEvolutionNumerical evolution of a nonlinear wave equation on general four-dimensional static, spherically symmetric, and asymptotically flat (horizonless) spacetimes.
ankitvb / Shock TurbulenceCompute spherically expanding and converging shock waves through isotropic turbulence and perturbed material interfaces
griffinfoster / SWHTPython implementation of the Spherical Harmonic Wave Transform for Radio Interferometry
garciapomar / Multipole Decomposition Of Dielectric Nanoparticles In A Hexagonal ArrayThese files of COMSOL and MATLAB make the calculation of the electromagnetic fields for a hexagonal array of Silicon hemispheres over a dielectric (mesoporous titania) array of cylinders with the same arrangement over a base layer of the dielectric on top a silica substrate. The multipole decomposition of the particles is made and the surface energy and reflectance are calculated. If you use this files, please, cite as Molet, P., Gil-Herrera, L., Garcia-Pomar, J., Caselli, N., Blanco, Á., López, C., & Mihi, A. (2020). Large area metasurfaces made with spherical silicon resonators, Nanophotonics, 9(4), 943-951. doi: https://doi.org/10.1515/nanoph-2020-0035 Steps 1. Create a folder called C:\datacomsol\ 2. Change the geometric and materials parameters if you want. 3. Run the COMSOL files (v.5.5) (_RF for Radiofrequency module or _WO for Wave optics module). 4. Clear all the tables in Tables. 5. Evaluate all the derived values. 6. Export all the data in Export. 7. run Matlab file (v.2010b).
