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Contact | Schedule of current classes | Hungarian
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Professor Emeritus
Institute of Applied Mathematics
John von Neumann Faculty of Informatics
Doctoral School of Applied Informatics and Applied Mathematics
Óbuda University
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Horváth László pages
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Courses
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Courses in
English
Click
to the name of the course for information about the course, syllabus
and teaching materials.
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Program
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Concentration
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Course
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Type
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Last
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Next or
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Engineering
Informatics MSc
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N, in
English
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Spring 2016
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Engineering
Informatics and Applied Mathematics MSc
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Elective
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System Level Modeling
for Cyber-Physical Engineering Structures in the Cloud
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N, in
English
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New subject
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Mechatronic
Engineering MSc
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N, in
English
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Spring 2016
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Mechatronic
Engineering MSc
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N, in
English
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New in English
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Engineering Informatics BSc
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Elective
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N, in
English
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Spring 2015
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Engineering Informatics MSc
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Elective
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N, in English
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Spring 2014
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Spring 2020
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Computation
engineering doctoral
subprogram
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Subject for
AIAMDI PhD students
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In English
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New in English
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As demanded
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Computation engineering
doctoral subprogram
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Subject for
AIAMDI PhD students
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In English
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New in English
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Computation engineering
doctoral subprogram
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Subject for
AIAMDI PhD students
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Contextual
definition and representation of engineering objects
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In English
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New in English
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Syllabi
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Introduction
to Virtual Engineering
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Program
IT for Engineers, BSc
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Concentration:
Criterion
course, elective
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Type
Full time
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Purpose and objective
One of
the largest application areas of information and computer science and
technology is lifecycle management of product information. Increasing
percentage of information engineers find carrier in special purpose
networked product lifecycle management (PLM) system environments at
extended and international companies. Product information is
represented in virtual spaces. Purposeful software serves highly
integrated modeling of products, processes and factory environments.
This course is designed to help students understand fundamental
concepts, ideas, methods, techniques, and applications of virtual
engineering. It is an introduction to computer model descriptions of
interrelated bodies in a virtual space and applications of this shape-centered
modeling at digital definition of products.
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Topics of lectures
Click the
title of lecture to access the .ppt file.
Laboratory
hours include individual work of students in engineering virtual
space according to the syllabus below.
Laboratory system:
V6 PLM of the Dassault Systémes, Inc.
Virtual
engineering
Lecture
Role of virtual systems in engineering.
The virtual technology. Representation of physical
worlds. Product model and its components. Area dependence of
virtual spaces.
Laboratory
Overlook of an engineering virtual space. Functionality
of a complex engineering system. Contextual object definition.
Product structure and its application at model changes.
Solid modeling
Lecture
Boundary representation. Topological structure and rules.
Euler rule and its application .
Laboratory
Creating free form and generative curves.
Creating surface and solid in the context of a curve. Connecting
surfaces. Understanding concept lump.
Definition of
geometry
Lecture
Characteristics and representation of parametric rational
B-spline curves. representation. Polynomials. Continuity.
Laboratory
Creating points and curves by using of typical methods.
Modification of shape of curve and surface by control points.
Curvature and parameter analysis.
Modification of
shape by form features
Lecture
Construction of complex shapes by modification of an initial
shape. Three leveled description of form features. Local and global
modifications.
Laboratory
Creating volume adding,, volume subtracting, surface,
and conditioning surfaces as modifying shapes of a
solid.
Positioning and
moving related shapes
Lecture
Positional relationships of solid shapes in model
space Keeping constraints at modification of related solid
shapes. Creating mechanisms by adding relative movements to solid
shapes.
Laboratory
Placing and modification of relationships at connection of
components. Including an existing component in an assembly.
Finite element
mesh and load definition
Lecture
Concept of finite element analysis. Completing part models
for finite element analysis. Finite elements and placing
loads for analysis. Concept of associative, parametric, and
adaptive mesh.
Laboratory
Introduction by studying typical FEA tasks. Creating
and studying finite element mesh on a spatial surface. Definition
and optimization of a solid part and its meshing.
Processing
point clouds into surfaces
Lecture
Reverse engineering for the definition of surface using
measured points . Creation clouds of measured points. Principles
and methods for the processing of clouds.
Laboratory
Creating curves. Editing and filtering clouds of points.
Projecting curve to cloud.
Shape development
techniques
Lecture
Fillets on solids. Swept surfaces.
Laboratory
Creating fillet on solid. Creating intersections. Creating
variable offset surface.
Modeling of
engineering practice
Lecture
Challenges and possibilities in virtual space. Expert-like
capabilities. Optimizing shape.
Laboratory
Studying knowledge ware functionality. Understanding rule,
check, and control.
Visualization and
animation of objects
Lecture
Basics of visualization purposed computer graphics.
Shader model, methods of shading and rendering.
Modeling of light sources. Animation of position, shape, and light
sources.
Laboratory
Functionality of shape modeling system for
visualization of surfaces. Visualizations for the assistance
of model construction.
Circuit board
definition
Lecture
Circuit board definition in product model. Shape modeling in
the circuit board context. Constraint areas. Components.
Laboratory
Circuit board definition.
Development
of contextual chains
Lecture
Contextual curves and surfaces. Solid in the context of
surface. Definition and connection in contextual chains during
engineering work. Analyses of curves and surfaces in order to the
demanded geometry
Laboratory
Definition of complex solid through chains of contextual
connections.
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Situation
and Event Driven Models for Multilevel Abstraction Based Virtual
Engineering Spaces
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Program
IT for Engineers, BSc
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Concentration:
Criterion course,
elective
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Type
Full time
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Purpose and objective
The
course gives an introduction to theory, methodology, and systems
engineering of multidisciplinary product definition where self adaptive situation and event driven models
are applied in engineering virtual spaces. This latest industrial
product definition technology applies the four leveled abstraction of
the RFLP (Requirement, Functional, Logical, and Physical) structure.
In the course, most of the topics are explained and laboratory
experienced in the V6 PLM virtual engineering system (Dassault Systémes).
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Topics of lectures
Click the
title of lecture to access the .ppt file.
Laboratory
hours include individual work of students in engineering virtual
space according to the syllabus below.
Laboratory
system: V6 PLM of the Dassault Systémes,
Inc.
Abstraction in
product definition
Requirement, function, logical, and physical abstraction
(RFLP) based product definition. Generic and instance product
object representation.
Feature driven
contextual modeling
Principle and modeling of features. Feature driven
contextual product model.
Boundary
representation of solids
Topoloy and
geometry. Build up of topology. Non
uniform rational Bspline (NURBS) curves
and surfaces. Positional and DOF connection of solids.
Active knowledge
driven product definition
Situation and event based driving of feature definition.
Representation of rule, action, and reaction sets as expertise and
experience based active knowledge in product model.
Behavior of
product
Concept of behavior and its modeling in the RFLP structure
as reaction of the model system to events.
Mapping physical
level objects
Connection of product entities to different RFLP abstraction
levels. Virtual execution of product model.
Control of shape
in Boundary Representation
Contextual definition of surfaces. Generation rules and
contexts in surface definition and integration. Surface based
features in solids.
Functions
Connecting parts and their features in product model by
functions. Product function related analysis.
Analyses
Analysis of surfaces and curves. Modeling and analysis on
the finite element principle. Analysis of part placing and DOF.
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Adaptive
generic representations of engineering objects
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Program
Computation
engineering doctoral subprogram
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Concentration:
Subject for AIAMDI
PhD students
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Type
Doctoral subject
in connection with doctoral research of PhD student
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Purpose and objective
This
course introduces basic knowledge and laboratory experiences for research
in adaptive generic engineering models. Recent modeling capabilities
of industrial engineering modeling systems for analysis and
simulation in feature driven shape representation, driving model
along contextual chains, active knowledge and object behavior
representations, real time simulations, optimizing, experiments
planning are recognized and understood. This program is part of
preparing students for fundamental and engineering problem oriented
research at industries involved in products with high intellectual
content. Student collects material for examination in accordance with
individual research plan.
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Topics
Product model
Definition in accordance with ISO 10303. Three levels of
model structure. Generic and application resources. Application
protocols.
Object model
Principle of object orientation, classes and instances, and
concept of taxonomy.
Boundary
representation and connections of shapes
Topologically structured rational B-spline (NURBS) geometry.
Connection of solids using positional and DOF definitions.
Topological operators. Analysis for topological consistency.
Principle
of feature driving
Driving model by feature as object. Active and passive
features. Role of topology in case of driving by form features.
Connection of feature parameters using mathematical formulas and
functions.
Behavior of
engineering objects
Concept of product object behavior. Driving model definition
by behavior. Examples from mechanical and electrical behaviors.
Active
knowledge representations
Situation and event based driving of model. Simple active
knowledge representations: rule, action, and reaction sets.
Finite element model and its analysis
Approximation of design parameters by their calculation on
nodes in contextual finite element mesh. Principle of multiphysics
simulation.
Optimizing
Optimization and its types, optimized parameter, free
parameters, and optimizing algorithms.
Functions
Functional representation of engineering structures.
Functional actions.
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Contextual
definition and representation of engineering objects
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Program
Computation engineering doctoral
subprogram
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Concentration:
Subject for AIAMDI PhD students
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Type
Doctoral subject in connection with
doctoral research of PhD student
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The course is in the field of engineering which applies computer
models for theoretically grounded and at the same time experience
verified object modeling. The main teaching outcome is to introduce
scientific level application of object-oriented representations at
engineering activities which use complex self-adaptive model during
the whole innovation cycle of an industrial product. Student
understands definition of generic object model which uses object
parameters in contextual chains. Course includes the essential
principles and methods which constitute the background of contextual
model object definition, generation, and operation. Practical aspect
of the course is based on methodology applied at the V6 engineering
modeling platform by Dassult Systémes. This platform was the principal
modeling environment at the design of aircraft Airbus A350.
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Topics
and issues
Objects
in engineering model
Role
and place of object-oriented engineering model. Implementation of
object orientation in representation of engineering objects.
Definition
of contextual object model
The
context concept and its interpretation in engineering model.
Definition of context between relevant object parameters. Chains of
contexts and their role in active adaptive generic models.
Contextual consistency of model.
Boundary
representation of bodies
Basic
concept of topologically structured geometry at shape modeling.
Eulerian topology, topological entities, and topological
consistency. Local and global Euler operators. NURBS representation
of geometry: base functions, segmentation, and parametrization.
Contexts
for driving object parameters in feature-based engineering model.
Contexts
in engineering model: characteristics, driving, and main types.
Characteristics of feature driven shape model. Boundary
representation of form features.
Relating
parameters in structure of engineering objects
Representation
of context between relevant object parameters. Optimizing object
parameters: procedure, algorithms and place in model. Role of
knowledge representation in parameter connections. Relating loads
on objects and object performance parameters using method of finite
elements.
Knowledge
representation in engineering model
Production, control, action, and reaction
types of knowledge representation and their role in self-adaptive
generic model definitions. Role of hard and soft computing methods.
Representation of dynamic and shape behaviors.
Contexts
between objects in physical and virtual worlds
General
scenario. Model representation of physically existing shape object:
cloud of points, mesh, scan, and generation of curves. Role of
object parameters in cyber-physical system (CPS).
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Multilevel
abstraction based multidisciplinary models of engineering systems
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Program
Computation
engineering doctoral subprogram
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Concentration:
Subject for AIAMDI
PhD students
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Type
Doctoral
subject in connection with doctoral research of PhD student
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Purpose and objective
The
course introduces into system based representation of products as
multidisciplinary engineering structures. It includes important
issues from the latest engineering modeling where practice
orientation is harmonized with strong theoretical, methodological,
and system grounded fundamental, product related, and engineering
problem solving purposed research.
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Topics
Multidisciplinary
model
Need for integration of disciplinaries in engineering model.
Modeling engineering structure as system. Role of behavior
representation. Collaboration of disciplines in model and its
development.
Model
in RFLP structure
RFLP structures engineering model. Connections within and
between levels. Integration and main types of behaviors. Components
on the RFLP structure levels and definition their connections
Knowledge
driven adaptive model
Role and place of knowledge considering system based model
of engineering structures. Groups of knowledge representation and
role of knowledge in each group.
Coordinated
and structured simulations
Multiphysics simulations and coupled behavior between
multiply physical responses. Contextual chains of simulations.
Simulation structure in multidisciplinary engineering model.
Optimizing
object parameters
Optimization process and algorithms for contextual
engineering model. Role of constraints. role, function, and
characteristics of one of the algorithms selected in accordance
with PhD research.
Methods
for analysis of geometry and topology
Topology analysis based modeling on the physical level.
objects. Possibilities for topology based analysis in
research purposed laboratory work.
Management
of virtual product model
Special processes and management functions for complex
virtual representation of multidisciplinary product considering
RFLP structure.
Knowledge
representations
Knowledge representations in coordination with groups in
topic "Knowledge driven adaptive model". Integration of
theory and practice in virtual space. Integration of intelligent
computing methods.
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Modeling and Design
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Program
Engineering
Informatics MSc.
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Concentration:
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Type
Full time
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Outcomes
.Course
introduces recent engineering purposed virtual technology and systems
for this purpose. By now, engineering applies one of the most
comprehensive integrated information systems. Despite their key role
in advanced industry, theories, methodologies and systemics in these
systems are less understood by information technologists. Purpose of
the course is to fill this gap and introduce relevant important
modeling, simulation, knowledge technology, and systems engineering
theories and .methodologies. Emphases are on key virtual
technologies such as active generic models based on knowledge
representation and context chains, and representation of product
structures as multidisciplinary systems. Besides representation of
industrial products and experimental configurations using systems
engineering, mathematics, cross-disciplinary definition, and
knowledge technology, course introduces basics of organization of the
demanded modeling and simulation capabilities by industries,
discipline, and role. Model space is opened for each student in a
world class engineering system during lectures and laboratories in
order to better understand contemporary virtual engineering.
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Topics of lectures
Click the title
of lecture to access the .ppt file.
Laboratory
hours include individual work of students in engineering virtual
space according to the syllabus below.
Laboratory
system: V6 PLM of the Dassault Systémes,
Inc.
Virtual
World for Engineers
Key concepts to understand engineering modeling. Modeling
industrial product as system using RFLP structure. Way to Virtual
Engineering Space. Object model of a solid body. Information
integration. PLM model base. Cross-disciplinary development of
multidisciplinary products. Cyber-physical-biological systems.
System of Systems issues.
Representation
of shape in product model
Analytical, rule driven, and free form shapes. Boundary
representation: topology and geometry. Topology in solid body
representation. Definition of topological structures by Euler
operators. Form feature driven shape definition. Connections of
shape with its information environment.
Connections
between model entities
Associativities. Contextual connections. Geometry and dimension driven
shape. Constraints and their saving at modification of model.
Definition of surfaces and bodies in the context of curves and
contours. Connections between bodies.
Representation
of geometry
Approximation and interpolation curves and surfaces.
Parametric equation of curve and surface. Spline base functions.
Characteristics of segmented B-spline curves and surfaces. Handling
curve and surface parameters, knot vector. Variants of B-spline
curves. Unified geometry using NURBS representation.
Definition
and analysis of product behavior
Analysis of place dependent parameters by finite element
method. FEM/FEA. Analysis of collisions and DOF. Animation of
position and shape of solid bodies. Shape optimization. Manikin
modeling for ergonomic analysis.
Human
and computer
Hunan-computer interactions. Thinking and seeing in a
virtual space. Visually realistic modeling of surfaces. Tangible
reality.
Cross
Disciplinary Definition
Modeling Printed Circuit Board.
Constraint area definition. Electrical and mechanical
components. Placing components. Definition of traces.
Connection
between real and virtual world objects
Virtual reconstruction of physical surfaces from cloud of
points. Activating and filtering of a cloud. Mesh and its
irregularities. Scan and methods of its creation. Generating curve
on scan. Creating the free edges of a mesh. Ergonomic analysis
using manikin model in the context of product model. Manikin
attribute organization. Human posture analysis. Human activity
analysis. Human measurements.
Representation
of engineering knowledge
Role of knowledge in product model. Knowledge ware and
Intelligent property. Representation and reuse of results and
experiences. Combinations and relationships of parameters.
Application of methods of knowledge representation. Rule, check,
reactions, and formulas and their sets. Optimizing parameters.
Modeling
product as system
System modeling. RFLP Structure. Dynamic behavior.
State logic behavior. Behavior in a component. Virtual execution.
Architecture of a logical system. Pathway sets. Implement
relations. Simulations.
Modeling
robot systems
Modeling the robot mechanical system. Engineering
connections (joints). Jogging a mechanism. About robot motion
controller. Robot controller properties. Robot data profiles. Home
positions of robot. Robot motion profile. Motion planning. Generic
inverse kinematics at robots. Task design
Functional
modeling of shape
About functional shape modeling. Entities in functional
shape model. Functional behaviors. Functional features.
Product
data management and exchange
Product data management. Characteristics of data in
engineering virtual space. Engineering modeling specific data base
management functions. Product structure. Data exchange between
modeling systems. Development of data exchange standards. Initial
Graphics Exchange Specification. Product model standard.
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CAD Systems
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Program
Mechatronic
Engineering MSc
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Concentration:
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Type
Full time
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Outcomes
The course
program includes systems and their functionality for current advanced
information technology as it applied at leading engineering systems
for lifecycle management of product information. The main objective
is to understand multidisciplinary aspect of model representation
mediated engineering. Students receive related methodology,
functionality, and system related knowledge during laboratories
using leading industrial engineering modeling system with high
level knowledge representation capabilities.
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Topics of laboratories
Click the
title of lecture to access the .ppt file.
Laboratory
hours include individual work of students in engineering virtual
space according to the syllabus below.
Laboratory
system: V6 PLM of the Dassault Systémes,
Inc.
Systems for
Lifecycle Management of Product Information
From physical representations to RFLP structure. History of
integration. Object modeling. Functional modeling. Feature driven
modeling. Product information in model – Exercise CS01.
Functionality of
engineering modeling systems
Functionality of PLM systems. Collaboration of engineers.
Engineering model definition and knowledge reuse. Definition and
simulation of manufacturing. Organized realistic multiphysics
simulation. Development of generic engineering model – Exercise
CS21. From the shape modeling practice – Exercise CS22.
Shape Modeling by Form
Features
Solid bodies in model space. Form features and their
boundary representation. Combination of solid bodies. Modification
of shape using topological entities.
Shape centric
representation. Unified representation of geometry (NURBS) and
topology.
Polyhedron modeling and its application at topological
definitions. B-spline curves and surfaces. NURBS representation of
geometry. Euler rules and operators.
Representation of
mechanism
Engineering connection between solid bodies – principle and
parameters. Engineering connection with user defined
constraints. Engineering connection with predefined
type. Engineering connection between products. Definition of
mechanism representation. Simulation of mechanism. PLM model base
related considerations.
Definition
features for printed circuit board (PCB)
PCB board features and their definition. Constraints.
Electronic part representation. Mounting capacitors,
resistors, etc. on board.
Analysis on the
principle of finite elements (FEM/FEA)
Purpose and applications of FEM/FEA in PLM systems.
Preprocessing: definition of mesh, element, load, and restraint.
Procedure and evaluation of analysis.
Functional
shape modeling
About functional shape modeling. Functional behaviors.
Entities in functional shape model. Functional features.
Processing
cloud of points into
curve and surface
Reverse engineering for the definition of surface using
measured points. Gaining shape information from physical objects.
Processing clouds of scanned points into curve and surface
representations. Filters. Meshing. Concept and types of scan.
Contextual
connections in shape model
Case study which organizes and represents main types of
contextual connections in boundary represented and form feature
constructed shape model. Students analyze active model in leading
and theoretically and methodologically high level modeling system.
Model data
exchange between different models. ISO 10303.
Product and model data exchange. Main formats. Reference
model, resource, and application protocol (AP) based modeling when
different modeling and model format are applied in different PLM
systems in a project .
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System Level Modeling for Cyber-Physical Engineering Structures in the Cloud
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Programs:
Engineering
Informatics and Applied Mathematics MSc.
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Concentration:
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Type
Full time
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Course
outcomes and description
Course consists of selected issues in latest principles,
methods, and systemics of contemporary engineering informatics based
on the capabilities of the world level representative and
industrially proven 3DExperience platform. Its content is about
fundamentals in one of the largest, most complex and highest-level IT
applications! The main purpose of this subject is to understand and
experience theoretical, methodological, and systemics basics in model
of systems operated cyber-physical engineering structure related
issues such as representations for the integration of theory and
practical experience, multidisciplinary system-level models, generic
self-adaptive object models, realistic simulation structures, as well
as organic shape models and their relations with additive
manufacturing methods. To enable students to learn and experience the
engineering IT technology which is one of the basics of engineering
activities at the two largest aircraft producer and at many other
leading industrial companies, the work during lecture and laboratory
hours proceeds in a project which serves the course in the
3Dexperience system. The 3Dexperience is available at the Dassault's Systémes cloud for the Doctoral School of Applied
Informatics and Applied Mathematics (AIAMDI), Óbuda
University. The 3DExperience platform is regarded as the flagship of
engineering systems. It is product of development and market leader
Dassault Systémes, which operates and
continually updates our 3DExperience system in the cloud. Project
participants have access to personalized modeling capabilities based
on field of activity, current disciplines, and human roles. The
primary purpose of the new 3DExperience system is to provide
state-of-the-art laboratory research environment for the newly accredited
doctoral program at the AIAMDI in the Dassault cloud. In this way the
course plays a role in the preparation of students for doctoral
research. Thus, the student participating at this course and
interesting in its issues can prepare doctoral school application
beyond providing topics for MSc degree thesis work. In the
laboratory, all workstations are specified to be suitable for the
3DExperience.
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Topics
Click the title to access the .ppt file for each topic.
Laboratory
hours include individual work of students in virtual engineering
space (VES) using laboratory system using personally available
workstations.
Laboratory
system: 3DEXPERIENCE platform and V6 PLM system by Dassault Systémes, Inc.
Models of
cooperating systems
Lecture: Models of cooperating systems operated cyber-physical engineering
structures (experimental configuration, prototype, industrial
product).
Laboratory: Introduction to the laboratory environment and the course
project in the cloud.
Engineering modeling
basics
Lecture: Engineering modeling basics: model space, coordinate
systems and transformations, modeling capabilities, model object,
shape representation, system of contexts, feature, functional and
logical concept model, behavior.
Laboratory: Analysis and understanding case study in thematic model in
the cloud for the issues of the lecture.
Shape model
Lecture: Shape model: boundary representation, topology, NURBS,
curves and surfaces, and form features.
Laboratory: Definition of thematic model and its analysis and
understanding in the cloud for lecture issues as individual work of
each student.
Component
connections
Lecture: Representation of component connections in
multidisciplinary engineering structures: constraints, degrees of
freedom, contexts.
Laboratory: Definition of thematic model and its analysis and
understanding in the cloud for lecture issues as individual
classroom work of each student. Deadline of title and short summary
for the assignment.
Realistic multiphysics
analysis
Lecture: Realistic multiphysics analysis of performance parameters in
engineering structures. New application of the finite element
method. Definition of structured simulations in model.
Laboratory: Analysis and understanding case study in thematic model in
the cloud for the issues of the lecture. Information about course work
for the assignment in cloud.
Modeling
capabilities
Lecture
and laboratory: Structure of modeling
capabilities on the example of 3DExperience.
Active knowledge
representations
Lecture:
Active knowledge representation. Optimization algorithms in model
structure. Problem solving in wide context. Connecting
representations of theory and experience. Integration of outside
solvers such as Dymola and Simulink.
Concept of intellectual property (IP).
Laboratory: Definition of thematic model and its analysis and
understanding in the cloud for lecture issues as individual
classroom work of each student.
System level model
in RFLP structure
Lecture: System level model in RFLP structure. Representation of
behaviors and their role at virtual execution of concept model.
Laboratory: Analysis and understanding case study in thematic model in
the cloud for the issues of the lecture.
Generic
self-adaptive shape model
Lecture: Generic self-adaptive shape model which can be driven from
the outside and capable of function related behavior.
Laboratory: Definition of thematic model and its analysis and understanding
in the cloud for lecture issues as individual work of each student.
Surface Definition
Lecture: Curves as contexts of surfaces. Surface generation by
interpolation of curves. Theory and practice of swept surface.
Blend surface with continuity definition. Tabulating curve along a
vector. Joining surfaces. Cloud of digitized points. Activating and
filtering a cloud. Generating mesh on cloud. Generating curve using
scan. Function-driven organic shapes.
Laboratory: Definition swept surface using two curves. Definition
surface interpolating three curves. Definition surface connecting
the above two surfaces. Definition join between the above three
surfaces. Definition solid between the join and its offset surface.
Model of robot
system
Lecture: Model of robot system. Representation of mechanical and control
systems. Structure, contexts, and simulations of robot model on the
example of relevant modeling capabilities in 3DExperience.
Laboratory: Developing model for assignment in the cloud project of the
course.
Engineering
modeling project. Course work
Lecture: Engineering modeling project in which modeling capabilities
are available for each participant in accordance with actual
industry, relevant disciplines and roles. Explanation in the
3DExperience project of the course.
Laboratory: Developing model for assignment in the cloud project of the
course.
Course work
Laboratory: Developing model for assignment in the
cloud project of the course.
Trends in virtual
engineering. Student presentations (mini conference)
Lecture: Actual trends in development of virtual engineering systems
and the relevant possibilities for carrier in engineering.
Laboratory: Presentation the assignment using model in cloud.
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Modeling
and simulation
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Program
Mechatronic
Engineering MSc
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Concentration:
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Type
Full time
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Outcomes
Course introduces advanced knowledge based engineering
modeling methodology in a laboratory system which represents world
class virtual engineering. Students work in personally opened model
spaces and contribute to student project by active modes using active
model definition environment. They understand modeling and associated
simulations using definition of highly integrated self-adaptive model
systems. These models are in possession of capability for
self-modification in case of changed requirements and situations.
Topics such as mechatronic specific multidisciplinary model,
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