The environment has the ability to work in automatic design and optimization of products. Implemented data processing and analysis using various methods

Design of an expent (DOE), distribution of the input population of variables, estimation of forecast accuracy

User DOE; Random; Sobol; Full factorial; Cubic-face-centered; Taguchi; Box-Benken; Montecarlo; Reduced Factorial; Latin Square; Latin Hypercube;

D-Optimal; Cross validation method; Constraint satisfaction problem

Decision making for multi-criteria optimization (MCDM):

Hurwicz criterion;

Linear algorithm;

GA algoriphm;

Minimax, savage mimimax regret criterion;

Algorithms, optimization methods:

DOE Sequence - direct enumeration of parameters;

MOGA II - genetic algorithm for multi-criteria optimization;

ARMOGA - genetic algorithm based on MOGA;

NSGA II - genetic algorithm for non-dominated sorting for multicriteria optimization;

NASH - an algorithm based on the Nash theory of non-cooperative games for multicriteria optimization;

B-BFGS - gradient algorithm;

SIMPLEX - search for a solution without the use of derivatives by the Nelder-Mead method;

Levenberg-Marquardt;

Simulated Annealing - model hardening algorithm (simulated annealing method);

1P1-ES - evolutionary strategy;

DES - evolutionary strategy for perfog criterion optimization with continuous variables;

MMES - evolutionary strategy for multicriteria optimization with discrete and continuous variables;

FMOGA II - version of the MOGA algorithm with improved convergence;

FSIMPLEX - Simplex version with improved convergence and the ability to solve multicriteria problems;

MOSA - a version of simulated annealing with the ability to solve multicriteria problems;

MACK - an algorithm for approximating response surfaces;

NLPQLP - Sequential Quadratic Programming (SQP) algorithm;

NLPQLP-NBI - Normal Boundary Intersection method + NLPQLP (algorithm with the ability to solve multicriteria nonlinear problems);

Multi-Objective Particle Swarm.

Metamodels (response surface approximation, RSM, approximate mathematical models), construction methods:

K-Nearest (Shepard-a method);

SVD (singular value decomposition);

Kriging, a regression analysis technique based on the work of Daniel K;

Parametric surfaces, polynomial regression;

Gaussian Processes - an approach to solving problems of regression analysis based on the work of Bezier (Bayesian);

Artificial neural networks, radial basis function,

Meta model validation tools.

6 sigma, quality management, Design for Six Sigma (DFSS):

Sigma quality (six sigma quality);

Failure modes and analysis of their impact (discards analysis);

Ishikawa diagram.

Probability density function;

Study of the relationship between variables, scatter chart, line, bubble chart, trend lines;

Data distribution, histogram, pie, cumilative plot;

Linear correlation analysis, correlation matrix, scatter matrix,

effects matrix;

Deteation of basic characteristics of samples, box-whiskers, Quantile-Quantile plot;

Calculation of the closeness of interaction of parameters;

Working with data samples of large dimensions, Student's test, analysis of variance (Bon-ferroni test, ANOVA);

Distribution fitting;

Cluster analysis methods:

- partitive clustering

- methods of hierarchical clustering - average-linkage, centroid-linkaga, complete-linkaga, single-linkage,

ward approach,

- k-means algorithm (K-Means Clustering), Forgy approach, Kaufman approach, Macqueen approach, random

- self-organizing map algorithm (SOMs),

- dendrograms

- Optimization of the shape of the inlet pipes

- Optimization of the cooling system

- Optimization of the air flow in the ee compartment

- Reducing vibrations

- Aerospace

- The task of optimizing the shape of a centrifugal compressor

- The task of optimizing the shape of an axial turbine and axial compressor

- General mechanical eeering

- Optimization of the injection molding process

- Process optimization metal casting

- Optimization of hot stamping technology

- Marine construction

- The task of optimizing ship lines, reducing hydrodynamic resistance

- Optimal rudder design

- Financial markets

- The task of optimizing the investment portfolio of shares

- Decision-making in the financial market

AMESim; AVL Boost; AVL Hydsim; Flowmaster; GT-Power; KULI; Wave Aspen PLUS; CHEMKIN; eta / VPG; LS-DYNA; MADYMO; RADIOSS; Mathematica;

Matlab; DEP; MS Excel; MySQL OpenOffice; Winbatch; ADAMS; Carsim; Dymola; RecurDyn; SIMPACK; Virtual.Lab; CADFix; CATIA; SolidWorks; I-DEAS;

UaphicsNX; Maxsurf; ProEeer; JMAG; AVL-Fame; ICEMCFD; GID; Gridgen; MSC Patran; Paramesh; Sculptor; AdvantEdge; Cadmould; Magma COMSOL Multiphysiscs (FEMLAB); Simulink; ANSYS CFX; ANSYS TASCflow; FIDAP; FLUENT; GAMBIT; AVL-Fire; Star-CD; Star-CCM +; Star-Design; ABAQUS

ANSYS; ANSYS Workbench; AVL-Excite; eta / VPG; MSC MARC; MSC NASTRAN; PERMAS; SAMCEF; STRAUS7; SYSNOISE Fieldview; Friendship; Icare;

NAPA4; Nu-SHALLO; RAPID; REVA; Shipflow Condor; GridEe; IBM LoadLeveler; LSF; NQS



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