Course : Digital twins and AI: concepts, uses and issuesSimulating and optimizing real systems with digital twins
Seminar - 1d
- 7h00 - Ref. JUM
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 | Understand the fundamental concepts of digital twins and artificial intelligence |
| Identify the opportunities, limits and challenges associated with their implementation |
| Identify relevant use cases in different sectors (industry, healthcare, aeronautics, etc.). |
| Analyze the key steps and tools needed to design an AI-integrated digital twin |
Intended audience
Strategic decision-makers, innovation project managers, technical team leaders, R&D engineers, digital or industrial transformation managers.
Prerequisites
Master the basics of applied mathematics, simulation and numerical modeling. A first exposure to Machine Learning is a plus.
Course schedule
1 Introduction to digital twins: concepts and fundamentals
- Definition and origins of the digital twin concept.
- The components of a digital twin: data, models, real-world connections.
- Differences between digital twins, simulation models and digital models.
- The role of surrogate models.
- Overview of emerging fields of application.
2 AI for digital twins
- What is artificial intelligence? Focus on Machine Learning.
- Synergies between AI and simulation: hybrid modeling, prediction, recalibration.
- Link to Big Data, virtual reality and data science.
- The role of computing resources (HPC, cloud, edge computing).
- Ethical and technical issues related to AI integration.
3 Methodology for implementing a digital twin
- Key design stages: from modeling to deployment.
- Data collection and integration (sensors, historical, synthetic).
- Choice of software tools and platforms (open source vs. industrial).
- Validation and maintenance of the twin under real-life conditions.
- Success factors and pitfalls to avoid.
4 Use cases and sector feedback
- Manufacturing industry: process simulation and optimization.
- Medicine and health: patient twins, personalized medicine.
- Aeronautics and transport: predictive maintenance, safety.
- Energy and environment: real-time monitoring, energy efficiency.
- Group discussion: opportunities for your business.
PARTICIPANTS
Strategic decision-makers, innovation project managers, technical team leaders, R&D engineers, digital or industrial transformation managers.
PREREQUISITES
Master the basics of applied mathematics, simulation and numerical modeling. A first exposure to Machine Learning is a plus.
TRAINER QUALIFICATIONS
The experts leading the training are specialists in the covered subjects. They have been approved by our instructional teams for both their professional knowledge and their teaching ability, for each course they teach. They have at least five to ten years of experience in their field and hold (or have held) decision-making positions in companies.
ASSESSMENT TERMS
The trainer evaluates each participant’s academic progress throughout the training using multiple choice, scenarios, hands-on work and more.
Participants also complete a placement test before and after the course to measure the skills they’ve developed.
TERMS AND DEADLINES
Registration must be completed 24 hours before the start of the training.
ACCESSIBILITY FOR PEOPLE WITH DISABILITIES
Do you need special accessibility accommodations? Contact Mrs. Fosse, Disability Manager, at psh-accueil@orsys.fr to review your request and its feasibility.
Strategic decision-makers, innovation project managers, technical team leaders, R&D engineers, digital or industrial transformation managers.
PREREQUISITES
Master the basics of applied mathematics, simulation and numerical modeling. A first exposure to Machine Learning is a plus.
TRAINER QUALIFICATIONS
The experts leading the training are specialists in the covered subjects. They have been approved by our instructional teams for both their professional knowledge and their teaching ability, for each course they teach. They have at least five to ten years of experience in their field and hold (or have held) decision-making positions in companies.
ASSESSMENT TERMS
The trainer evaluates each participant’s academic progress throughout the training using multiple choice, scenarios, hands-on work and more.
Participants also complete a placement test before and after the course to measure the skills they’ve developed.
TEACHING AIDS AND TECHNICAL RESOURCES
• The main teaching aids and instructional methods used in the training are audiovisual aids, documentation and course material, hands-on application exercises and corrected exercises for practical training courses, case studies and coverage of real cases for training seminars.
• At the end of each course or seminar, ORSYS provides participants with a course evaluation questionnaire that is analysed by our instructional teams.
• A check-in sheet for each half-day of attendance is provided at the end of the training, along with a course completion certificate if the trainee attended the entire session.
• The main teaching aids and instructional methods used in the training are audiovisual aids, documentation and course material, hands-on application exercises and corrected exercises for practical training courses, case studies and coverage of real cases for training seminars.
• At the end of each course or seminar, ORSYS provides participants with a course evaluation questionnaire that is analysed by our instructional teams.
• A check-in sheet for each half-day of attendance is provided at the end of the training, along with a course completion certificate if the trainee attended the entire session.
TERMS AND DEADLINES
Registration must be completed 24 hours before the start of the training.
ACCESSIBILITY FOR PEOPLE WITH DISABILITIES
Do you need special accessibility accommodations? Contact Mrs. Fosse, Disability Manager, at psh-accueil@orsys.fr to review your request and its feasibility.