The training is intended for people who are interested in virtualization and cloud technologies or who would like to deploy and manage efficiently applications within modern architectures.

INSTRUCTORS

The speakers are Artelys consultants with a solid experience in deploying and managing docker containers, Kubernetes clusters by multiple providers and serverless applications in production.

Docker

• • Main principles
  • My first Dockerfile
  • Introduction to docker-compose and docker swarm
  • Container as a Service principles and implementations in cloud
  • Other container technologies

Kubernetes

• • Main principles
  • My first managed K8S
  • Introduction to scalability and service mesh
  • Kubectl basics : how to control my Kubernetes cluster with command line
  • Brief overview on helm and some CNCF projects

Serverless

• • FaaS : main principles, advantages and drawbacks
  • Developing a high quality FaaS application
  • Available technologies in cloud and comparisons per language

• Introduce the basics of operating an electrical system.
• Describe the organization chosen in Europe to operate, control and ensure the safety of the system.
• Describe the structure of the European markets and their potential operational and strategic impacts.

This course is intended for students, consultants and energy analysts who whish to gain an understanding of the operation of power transmission systems.

Introduction to Power Systems Analysis
• General characteristics of power systems.
• Main components of power systems.
• Power system structure.

Power Flow Analysis
• Basic principle for circuit analysis.
• Power Flow Computation.
• Newton-Raphson method.

Regional Security Analysis
• Power system security.
• Coordinated Security Analysis.
• Line overloads.
• Cascading effects.
• Voltage collapse.
• Preventive/curative remedial actions.

System security
• Major incidents in France and in the world, typology of network collapses.
• Network protection: N-k, preventive and curative actions, defense plan.

Structure of a generation-transmission-consumption network
• Production, merit order principle.
• Use of electrical energy, characterization of consumption.
• Balance between production and consumption, notion of optimal adapted park.
• Need for an electrical network and structuring.

Interaction with the markets
• Energy Only market model and clearing algorithms.
• Structure of the electricity markets.
• Reserve markets, interactions with setting.
• Market couplings and regional security coordination.

Power System Control
• The basis of system control.
• Active power and frequency control: primary, secondary and tertiary control.
• Voltage and reactive power regulation: automatic voltage regulator, on-load regulator, reactive power injection.
• Power transits.

Practical work: Power Flow and security analysis in Python with PowSyBl / France-Spain capacity sizing with Artelys Crystal Super Grid.

• Present the challenges of decarbonization.
• Identify and characterize the main options for decarbonizing the electrical system and related systems (mobility, heat, hydrogen).
• Describe the structure of the European markets and their potential operational and strategic impacts.
• Introduce the fundamentals of the technical and economic analysis of these issues.

This course is intended for students, consultants and energy analysts who whish to acquire an overview of these key issues.

Power systems (status, trends, flexibility needs)
• Introducing a few useful concepts
• Status of the European power system.
• EU trends, energy and climate objective on production, consumption and GHG emissions.
• Systemic challenges.

Introduction to key economic concepts
• From investments to operations – a series of decisions.
• Presentation of electricity costs.
• Market revenues presentation
• Role of markets.
• Cost-benefit analysis methodology – measuring the impacts of an investment project and establishing/analyzing a transition trajectory, synergies and interdependencies between technological options.

Electricity markets in Europe
• Roles of electricity markets.
• Sequence of electricity markets.
• Brief comparison between US and EU systems.

Challenges, hydrogen and wrap-up
• Challenges for the future electricity system.
• Interactions between hydrogen and electricity systems.
• Wrap-up.

COURSE OBJECTIVES

• Introduce fundamental principles and good practices of HPC computation on distributed architecture.
• Understand the inherent stakes and difficulties involved in the design and integration of a decision support software through practical examples.
• Understand technologies used as well as their vocabulary.

TARGET AUDIENCE

• Architects, project managers, or technical experts aiming to grasp the specificities of quantitative computational function-based solutions (optimization, simulation, statistical learning).
• Decision support engineers aiming to expand their skills in computer science and software integration.
• Any developers involved in working on complex IT development projects.

Software Design
• Software quality factors.
• Code maintenance and maintainability.
• Unit tests and Test-Driven Development method.

Design Patterns
• General introduction.
• Gang of Four Design patterns in-depth study.
• Anti-Patterns.

Data structure options
• Introduction to computational complexity.
• Data collection and linkage structures.

Introduction to development tools
• Version control system, code review (examples: Git, Gerrit, Sonar).
• Continuous integration platform (Jenkins).
• Software integrator (Maven).
• Project manager (Redmine).
• Be able to use your IDE.
• Additional tools (Meld, unix/grep, etc.).

Designing an integrated decision support solution
• Fundamentals and issues.
• Technology options.
• Notion of highly cohesive low coupling.

Foundations and technologies of software architecture
• Architecture (client-serveur, SOA, etc.), SaaS solutions, remote computing.
• Data exchange methods and dedicated tools.
• Technical base of solutions, application containers, hosting.
• Interfacing a calculation engine in synchronous, asynchronous or hybrid mode.
• Achieve a downgraded mode.

Approach to conduct these projects
• V-cycle, agility, spiral or iterative development.
• Standard solutions, examples of architectures, and critical analyses.

COURSE OBJECTIVES

This course will include stochastic optimization problem modeling and processing with dynamic programming and derivative techniques. 

TARGET AUDIENCE

Anyone aiming to discover stochastic optimization through dynamic programming. 

Deterministic Dynamic Programming 
• Introduction.
• Deterministic dynamic programming: principles. Transition equation, state, Bellman values. Shortest path problems. Management of production unit start-ups.
• Bellman values and dual variables. Economical interpretation of Bellman values. Inventory management case. Link with dual variables. 

Stochastic dynamic programming 
• From deterministic to stochastic. Modeling a dynamic stochastic optimization problem. Non-Anticipativity constraints. Dynamic programming on scenario trees. Application to option pricing.
• Dynamic stochastic programming. State definition and probabilistic structure. Examples of modeling. Bellman values economic interpretation. Valuation of future contracts. 

Stochastic dynamic programming (continued) 
• Inventory management and dynamic programming: some examples. Modeling and effects on Bellman functions. 
• Large dynamic problems. Limits of dynamic programming for large problems. 
• Decomposition methods: dual dynamic programming, scenario decomposition, scenario tree method.
• Processing large dynamic problems: application to annual electricity production planning. Dynamic management modeling of interconnected systems in the energy sector. Resolution by decomposition. Solving by dual dynamic programming.
• Links with reinforcement learning. 
• Sampling and generalization techniques. 
• Dynamic learning and optimization schemes.

Training wrap-up 

COURSE OBJECTIVES

• Presenting the fundamental principles and best practices of HPC computing on distributed architectures. 
• Understanding the new challenges of HPC in the cloud. 
• Putting knowledge into practice on well-known examples from the scientific and industrial world.

This training is intended for engineers and researchers likely to use HPC and wishing to acquire a first experience or cluster users looking to update their knowledge on the latest technologies.

Presentation of High-Performance Computing 
• Main issues and need of parallelization.
• Application examples.
• Hardware and software components of High-Performance Computing (processors, memory, applications for the implementation of parallelization on the hardware).
• Performance measurement of calculations and improvement techniques.
• Introduction to parallelism performance indicators.

Architecture of HPC systems 
• Shared memory architecture. 
• Distributed memory architecture.
• Hybrid architectures.

High-performance architecture management systems: Scheduling and Load-balancing with a high-performance infrastructure management system.

• Performance metrics and monitoring.
• Overview of the main systems: SGE, SLURM, OpenPBS, Torque and Celery.
• Scalables cloud technologies: Container as a Service, K8S and Knative, Azure et AWS Batch, Parallel Cluster – GCP Cloud HPC Toolkit – Azure CycleCloud.

Setting up an HPC cluster:
• Setting up a local cluster with Docker.
• Launching first jobs.
• Installing apptainer.
• Building apptainer images.
• Launching containerized jobs.

• Develop skills in R software, from getting started to deploying forecasting modules.
• Gain a proven methodology for data analysis and development of a demand forecasting model.
• Learn about statistical techniques for forecasting: know the main ranges of models, their advantages and disadvantages, as well as the link with the business expertise.

TARGET AUDIENCE

This course is intended for people who work with a set of data (e.g., business analysts) and who are looking to improve their skills with a tool that allows them to perform advanced analyses. 

Getting started with R software 
• Main features and advantages. 
• Description of the syntax and the most useful keywords. 
• Good programming practices in R.
• Installation of the software and the working environment.

Illuminating the data and the problem 
• Getting to grips with the data by visualizing the chronicles.
• From raw data to usable data (data preprocessing methods).
• Identification of the structuring characteristics of the data (numerical and graphic indicators, seasonality, explanatory factors).
• Definition of the problem, the stakes, and the quality criteria of the forecasts.

Build a relevant demand model 
• Which models should be considered given the characteristics of the data? Presentation of several classical models: autoregressive models (S)AR(I)MA(X), linear models. 
• Implementation of the different models with the R software.
• How to choose a good model: analysis of the quality of the forecasting results (fitting performance, generalization).

To go further 
• Other types of models (frequency models).
• How to make relevant graphs (ggplot package).
• How to maintain in time your developments in R.

Fundamentals of Python programming
• Python data structures (lists, tuples, dictionaries).
• List traversal and generation (itertools, iterators, generators and comprehension lists).
• Good practices: exceptions, type checking, etc.

Code organization and quality
• Comments and cleanliness (docstring, linters, pep8, etc.).
• Modularity and reusability of code (file import, Object Oriented Programming and polymorphism).
• Algorithms and complexity.

Distribution, isolation, and package management

Introduction to scientific programming
• Scientific programming vocabulary and statistical analysis.
• Main machine learning algorithms (supervised analysis, unsupervised analysis, classification, and regression).
• The scientific stack: Numpy, Scipy, Scikit-learn, Pandas, Sympy, Matplotlib.

Descriptive statistics and data structures
• Data management with pandas: import, dataframes, slicing, mapping (reading, formats, date management).
• Visualization with Matplotlib.

Machine Learning with Scikit-learn
• Presentation, linear modelling and prediction, classification with Scikit-learn.

Scientific computing with Numpy
• Presentation, data structure, indexing, slicing, iterating.

Scientific computing with Scipy
• Overview, linear algebra, application.

COURSE OBJECTIVES

Whichever is your application domain, this training will provide you with an introduction to the field of nonlinear optimization and will teach you how to apply nonlinear modeling techniques to industrial applications using Artelys Knitro.

TARGET AUDIENCE

Scientists and developers interested in modeling and solving nonlinear programs using Artelys Knitro.

Nonlinear programming (NLP)

• Introduction, presentation of the training.

• Problem statement and optimality conditions.

• Newton method for unconstrained optimization. Globalization techniques.

• Interior-point and active-set methods for constrained optimization.

Solving nonlinear problems with programmatic interfaces

• Presentation, modeling and solving a nonlinear model with Artelys Knitro in Python™.

• Impact of exact versus approximate derivatives. Quasi-Newton method.

• Using Artelys Knitro in R/MATLAB®: a nonlinear least square minimization application.

Solving nonlinear problems with modeling interfaces

• Using Artelys Knitro in AMPL: modeling syntax, automatic differentiation, examples.

• Good practices in nonlinear modeling. Tips and tricks.

• Fine-tuning Artelys Knitro parameters.

• Global optimization using parallel multi-start.

Solving nonlinear models with special features

• Mixed-integer nonlinear programming (MINLP) methods. Practical example.

• Mathematical Programming with Equilibrium Constraints (MPEC). Application to computational economics and game theory.

• Convex non-smooth models.

COURSE OBJECTIVES

Mastering the principles of hybridization and decomposition methods in order to solve difficult problems of large size.

TARGET AUDIENCE

Engineers, economists, scientists and developers interested in modeling decision problems and implementing optimization algorithms.

Hybridization techniques

• Linear programming / Constraint programming hybridization. Mixed modeling, common search trees, dialogue among branching schemes. Using reduced cost.

• Constraint programming / Local search hybridization. Description of neighborhoods as constrained neighborhoods. Under constraints’ neighborhood exploration.

Decomposition techniques

• Principles of Price Decomposition and Resource Decomposition. Types of information exchanges. Elementary examples.

• Lagrangian. Duality. Definition and economic interpretation of the dual function. Duality gap. Convex and Non-Convex cases.

• Price Decomposition: coordination algorithms and Non-differentiable Optimization.

• Benders decomposition: principles and implementation.

Applications

• Joint gas and electricity assets optimization: introduction, Benders and price decomposition.

• Example of constraint programming and local search hybridization: timetables scheduling, frequency assignment.

• Decomposition and hybridization for maintenance scheduling.