Université Grenoble Alpes | University

Knowledge Integration and Collaboration in Design

This course aims at presenting the latest developments, theories and approaches in product development in a global and distributed world with a multi-expertise, multi-enterprise and multi-actor context. More precisely we adress here the research methodology issues related to the study of such complex collaborative situations. The aim of the course is to provide the latest tools for analysing and modeling complex collaborative situations.

http://genie-industriel.grenoble-inp.fr/fr/formation/ue-knowledge-integration-and-collaboration-in-design-wguknow9 Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Autres First situation: designing a ticket folder
Discovering design as a collaborative and knowledge intensive activity. Situation 2: delta design game integrating communities of practice
Discovering mutual understanding, shared representations in synchronous design situationsObserving Collaborative design situations.
Presentation of the main research methodologies accessible today. Focus on protocol analysis and coding exercise.Situation 3 : designing a real design experiment.
Among 3 themes one team will develop a complete protocol (brief, data collection, analysis grid)
*theme 1: early supplier involvement
*theme 2: User centered design and participatory design
*theme 3: Integration of environmental issuesSituation 4: lab work on a collaborative platform
Discovering the issue of communities of practice and asynchronous collaboration. Motivation to understand and analyse the collaborative design process.
Motivation to develop methodological skills of analysis and observation.
It is desirable that students have a good knowledge of the product design activity through students projects or work experience. 6 2nd year of master Lecture Course content can evolve at any time before the start of the course. It is strongly recommended to discuss with the course contact about the detailed program.

Please consider the following deadlines for inbound mobility to Grenoble:
– April 1st, 2020 for Full Year (September to June) and Fall Semester (September to January) intake ;
– September 1st, 2020 for Spring Semester intake (February – June). Final exam international.cic_tsukuba@grenoble-inp.fr

River Dynamics

First part: fluvial dynamics.
1.- Introduction on the floods and floodings. Relinders on open channel hydraulics Hydraulics.
2.- One-dimensional free surface flows: the Barré de Saint-Venant PDE equations. Physical meaning of the different terms of the equations. Mathematical properties: the characteristics and the invariants.
3.- Physics of floods and their modeling. Kinematic and diffusive approximation. Example: the deterministic runoff.
4.- Flood alleviating structures: physical principles and optimisation of dams by a costs-benefits analysis
5.- Rapidly varying unsteady open channel flows dominates by inertia: shock and rarefaction waves. Sudden stop of a flow and dam breakage.

Second part: sediment transport.
1. Fundamental concepts: the various modes of sediment transport, the materials, introduction to river morphology.
2. Elementary analysis of the bed-load mechanism: threshold conditions of sediment movement, sensitivity of Meyer-Peter and Muller formula. The Einstein formula
3. 1D analysis of sediment transport: predictive formulaes and their range of applicability.
4. Deeper in the mechanisms of the sediment transport: transport in suspension, grain roughness, shape roughness, dune and bed forms, secondary currents, transported grain size distribution and bottom grain size distribution, grain size sorting by the flow, hiding effects & armoring.
5. Morphological changes

http://ense3.grenoble-inp.fr/en/academics/river-dynamics-5eus5dyf-1 First part: fluvial dynamics.
Understand the physics and the modeling of unsteady flows in the rivers and canals (propagation of the tide, floods and of rapidly varying flows in the rivers and canals). Saint Venant equation formulation.
Design the volume of retention dams for flood protection.
Understanding the links between the physical reality, its perception and its modeling.
Brief presentation of the market software properties dealing with this problem.

Second part: sediment transport.
Students will become acquainted with the pluridisciplinary aspects of this topic.
Student will be asked to master: the concept and the quantitave determination of sediment mouvement inception, computation of sediment transport rates, the concept of sedimentary equilibrium (river bed slope, grain size distributions), engineering tools of the field Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Polygone scientifique First part: fluvial dynamics.
Understand the physics and the modeling of unsteady flows in the rivers and canals (propagation of the tide, floods and of rapidly varying flows in the rivers and canals). Saint Venant equation formulation.
Design the volume of retention dams for flood protection.
Understanding the links between the physical reality, its perception and its modeling.
Brief presentation of the market software properties dealing with this problem.

Second part: sediment transport.
Students will become acquainted with the pluridisciplinary aspects of this topic.
Student will be asked to master: the concept and the quantitave determination of sediment mouvement inception, computation of sediment transport rates, the concept of sedimentary equilibrium (river bed slope, grain size distributions), engineering tools of the field – Open channel hydraulics
– Fluid mechanics and turbulence
– Hyperbolic partial differential equations (characteristics)
– Statistics Eric Barthelemy 5 2nd year of master Lecture Course content can evolve at any time before the start of the course. It is strongly recommended to discuss with the course contact about the detailed program.

Please consider the following deadlines for inbound mobility to Grenoble:
– April 1st, 2020 for Full Year (September to June) and Fall Semester (September to January) intake ;
– September 1st, 2020 for Spring Semester intake (February – June). CT: 2x2h sitting exam;
CC: 2 practicals (TP) & a mini project (BE);
final mark: 75% CT + 25% CC international.cic_tsukuba@grenoble-inp.fr

Sustainable Manufacturing

There is no single common definition of sustainable manufacturing but the US Department of Commerce’s Sustainable Manufacturing Initiative
sums it up as: “The creation of manufactured products that use processesthat minimize negative environmental impacts, conserve energy and
natural resources, are safe for employees, communities, and consumers and are economically sound.”

http://genie-industriel.grenoble-inp.fr/en/studies/sustainable-manufacturing-5guc2304 Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Autres This course allows students to learn about the main principles and
main changes to come for sustainable manufacturing and finally to
understand how to implement the main methods and tools supporting
decision making for sustainability in Engineering. Students will
study in detail the production methods of the future. Concepts like
remanufacturing,circular economy, product service strategy and upgrading
are explained. Economic and legal instruments that initiate the
transition of firms are presented. Lectures are accompanied by a
projet (done in groups of 3-4) that aims at improving and testing the
knowledge of chosen concepts in particular industries for particular
products. At the end of the course, the student will be able to:

• Understand environmental issues for industries and firms
• Implement environmental assessment methods
• Define adapted industrial policies and strategies
Basics in product and production technologies.
Basic knowledge of Economics and Sociology of Organisation. Oliwia KURTYKA 6 2nd year of master Lecture Course content can evolve at any time before the start of the course. It is strongly recommended to discuss with the course contact about the detailed program.

Mechanical Structure Design

The different phases of the design of an hydraulic structure are presented in this course:
1. Geological survey, regarding both the mechanical and the transfer properties, of a specific site to build a large structure.
2. Pre-design of the structure based on analytical methods (design rules as Eurocodes for instance) with simplified hypothesis.
3. The detailed structural design based on advanced rheological laws for geomaterials (soils, rocks, concrete) and finite element modelling, including construction phase.

http://ense3.grenoble-inp.fr/en/academics/mechanical-structure-design-5eus5cmo-1 • Comprehension and modelling of mechanical behaviour of geomaterials
• Global understanding of the design phase of a structure
• Comprehension and modelling of a structure including its foundation. Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Polygone scientifique • Comprehension and modelling of mechanical behaviour of geomaterials
• Global understanding of the design phase of a structure
• Comprehension and modelling of a structure including its foundation.
Continuum Mechanics
Finite Element Method
Material Strength Theory
Rock and soil Mechanics Gael Combe 5 2nd year of master Lecture Course content can evolve at any time before the start of the course. It is strongly recommended to discuss with the course contact about the detailed program.

Session 1:
40% CC — 60% CT

Session 2:
No resit exam for CT

Continuous assessment is composed of:
– a report dealing with the design of a retaining wall using Eurocode 7 international.cic_tsukuba@grenoble-inp.fr

Digital chain for Industry 4.0 including virtual and augmented reality

The deployment of industry 4.0 is based on various innovative technologies but also requires the construction a complete digital chain
from the design office to enable these technologies. The digital chain in industry 4.0 depends on a relevant information system.The
lesson teaches how to deploy a complete digital chain from design office to production system applications based on virtual reality and
augmented reality applications.

http://genie-industriel.grenoble-inp.fr/en/studies/digital-chain-for-industry-4-0-including-virtual-and-augmented-reality-5guc3319 Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Autres The lesson is presenting 3 complementary aspects:data sources available in industry at design or production levelcreating an information system to connect and share various source of informationvirtual reality and augmented reality technologies and applicationsThe
role of the lesson is to learn how to connect these 3 aspects to build a
complete digital chain that can be deployed for industrial practise.Labworks learn non sql information systemsThey remind basics of scripting languages (python, C#)Students experience a wide set of VR/AR devices within prepared labworksStudents apply this knowledge to specify and create a digital chain to solve a specific industrial process. CAD/3D modeling: almost basic 3D modeling is expected. Students who have
few experiences about this pint will be oriented on basic tutorials
but it will not be part of the lesson activityComputer science
development basics : basics will be reminded but students will need to
develop few codes mainly in python or C# Frédéric NOEL 6 2nd year of master Lecture Course content can evolve at any time before the start of the course. It is strongly recommended to discuss with the course contact about the detailed program.

Natural Hazards and Soil Improvements

This course provides an overview of the issues natural hazards and climate change on the stability of structures (especially related to the transition between unsaturated and saturated soil). It covers in particular the seismic risk and the slope stability. The physical and mechanical phenomena induced in the soil and structures are addressed. The consequences in terms of stability of structures and natural slopes are analyzed. Finally the techniques usually used for risk mitigation (including soil improvement or installation of protective structures) are identified and discussed in more depth in the context of a design project.

http://ense3.grenoble-inp.fr/en/academics/natural-hazards-and-soil-improvements-5eus5rnc-1 To understand the tools used for defining the regional and local seismic hazard
• Advanced knowledge of soil dynamics and the associated mechanical phenomena (including liquefaction)
• To know how to design a structure considering the seismic hazard (including dams, natural slopes, retaining structures and foundations)
• Knowledge of physical and mechanical phenomena at the origin of gravity risk (landslide, rock fall, avalanche)
• To understand the behavior of unsaturated soils and the impact on the stability of structures or natural slopes
• Knowledge of the main techniques used for improving soil and the design of protective structures Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Polygone scientifique • To understand the tools used for defining the regional and local seismic hazard
• Advanced knowledge of soil dynamics and the associated mechanical phenomena (including liquefaction)
• To know how to design a structure considering the seismic hazard (including dams, natural slopes, retaining structures and foundations)
• Knowledge of physical and mechanical phenomena at the origin of gravity risk (landslide, rock fall, avalanche)
• To understand the behavior of unsaturated soils and the impact on the stability of structures or natural slopes
• Knowledge of the main techniques used for improving soil and the design of protective structures Soil and rock mechanics, groundwater flow, rheology, structural dynamics. Fabrice Emeriault 5 2nd year of master Lecture Course content can evolve at any time before the start of the course. It is strongly recommended to discuss with the course contact about the detailed program.

Data Management in large-scale distributed systems

In this course, we will study the fundamentals and research trends of distributed data management, including distributed query evaluation, consistency models and data integration. We will give an overview of large-scale data management systems, peer-to-peer approches, MapReduce frameworks and NoSQL systems. Ubiquitous data management and crowdsourcing will also be discussed.

Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Domaine universitaire – Saint-Martin-d’Hères Data management and knowledge extraction have become the core activities of most organizations. The increasing speed at which systems and users generate data has led to many interesting challenges, both in the industry and in the research community. Fundamentals of DBMS, parallel programming (threads) Thomas Ropars 3 2nd year of master Lecture Course content can evolve at any time before the start of the course. It is strongly recommended to discuss with the course contact about the detailed program.

Advanced Learning models

Statistical learning is about the construction and study of systems that can automatically learn from data. With the emergence of massive datasets commonly encountered today, the need for powerful machine learning is of acute importance. Examples of successful applications include effective web search, anti-spam software, computer vision, robotics, practical speech recognition, and a deeper understanding of the human genome. This course gives an introduction to this exciting field, with a strong focus on kernels methods and neural network models as a versatile tools to represent data

http://ensimag.grenoble-inp.fr/fr/formation/advanced-learning-models-wmms536i-1 Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Domaine universitaire – Saint-Martin-d’Hères Introduction to statistical learning theory and kernel-based methods. Fundamental notions in linear algebra and statistics. Basic programming skills to implement a machine method of choice encountered in the course from scratch Julien Mairal – Xavier Alameda 3 2nd year of master Lecture Course content can evolve at any time before the start of the course. It is strongly recommended to discuss with the course contact about the detailed program.

High Performance computing for mathematical models

In this course, we will introduce parallel programming paradigms to the students in the context of applied mathematics. The students will learn to identify the parallel pattern in numerical algorithm. Introduction to parallelism, Models of parallel programming, Programming tools: OpenMP, OpenMPI

http://ensimag.grenoble-inp.fr/fr/formation/high-performance-computing-for-mathematical-models-5mm253c3 Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Domaine universitaire – Saint-Martin-d’Hères Applications in bioinformatics, computer vision, text mining, audio processing, etc. C or C++, Compiling, Data structures, Architecture, Concurrency Christophe Picard 3 2nd year of master Lecture Course content can evolve at any time before the start of the course. It is strongly recommended to discuss with the course contact about the detailed program.

Please consider the following deadlines for inbound mobility to Grenoble:
– April 1st, 2020 for Full Year (September to June) and Fall Semester (September to January) intake ;
– September 1st, 2020 for Spring Semester intake (February – June). Practical work international.cic_tsukuba@grenoble-inp.fr

Distributed Systems

Review of core principles of distributed systems. Characteristics and design issues of distributed systems, briefly revisiting the basic notions on network support, naming and binding.Main concepts and terminology of fault tolerance, including replicated servers for high-availability. Peer-to-peer Distributed Systems. Both structured and unstructured P2P architectures and designs.

http://ensimag.grenoble-inp.fr/fr/formation/distributed-systems-wmm535e-1 Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Domaine universitaire – Saint-Martin-d’Hères The objectives of this course are:

– to study the basic concepts used to design distributed algorithms: processes, failures, etc.
– to study a set of distributed algorithms that are used in modern distributed systems.

At the end of the course, the student will be familiar with a set of widely used algorithms. In particular, the following families of algorithms will be introduced: consensus algorithms, broadcast algorithms, synchronisation algorithms, etc. Basic notions of operating systems.
Basic notions of networks. Vivien Quema 3 2nd year of master Lecture Course content can evolve at any time before the start of the course. It is strongly recommended to discuss with the course contact about the detailed program.

Machine Learning Fundamentals

Machine Learning is one of the key areas of Artificial Intelligence and it concerns the study and the development of quantitative models that enables a computer to perform tasks without being explicitly programmed to do them. Learning in this context is hence to recognize complex forms and to make intelligent decisions. Given all existing entries, the difficulty of this task lies in the fact that all possible decisions is usually very complex to enumerate. To get around that, machine learning algorithms are designed in order to gain knowledge on the problem to be addressed based on a limited set of observed data extracted from this problem.

http://ensimag.grenoble-inp.fr/fr/formation/machine-learning-fundamentals-wmm9mo21 Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Domaine universitaire – Saint-Martin-d’Hères The intent of this course is to propose a broad introduction to the field of Machine Learning, including discussions of each of the major frameworks, supervised, unsupervised and semi-supervised learning. Massih-Reza Amini 3 2nd year of master Lecture Course content can evolve at any time before the start of the course. It is strongly recommended to discuss with the course contact about the detailed program.

Fundamentals of Probabilistic Data Mining

This lecture introduces fundamental concepts and associated numerical methods in model-based clustering, classification and models with latent structure. These approaches are particularly relevant to model random vectors, sequences or graphs, to account for data heterogeneity, and to present general principles in statistical modelling.

Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Domaine universitaire – Saint-Martin-d’Hères Model-based clustering, classification and models with latent structure are particularly relevant to model random vectors, sequences or graphs, to account for data heterogeneity, and to present general principles in statistical modelling. The following topics are addressed:

Principles of probabilistic data mining and generative models; models with latent variables
Probabilistic graphical models
Mixture models and clustering
PCA and probabilistic PCA
Generative models for series and graphs : hidden Markov models Fundamental principles in probability theory (conditioning) and statistics (maximum likelihood estimator and its usual asymptotic properties).
Constrained optimization, Lagrange multipliers. Jean-Baptiste Durand 3 2nd year of master Lecture Course content can evolve at any time before the start of the course. It is strongly recommended to discuss with the course contact about the detailed program.