Domain randomization course in aerospace AI
About our
The Domain Randomization in Aerospace AI course explores the application of this technique for training artificial intelligence (AI) models in complex aerospace scenarios, such as autonomous navigation and anomaly detection. It focuses on generating synthetic data with random variations in the environment (domain), improving the robustness and generalizability of machine learning algorithms. It includes 3D environment simulation, sensor modeling, and performance evaluation in real-world challenges. It is linked to areas such as computer vision, predictive control, and aerial robotics, optimizing the development of AI systems for aeronautical applications. The course offers hands-on experience using AI tools and libraries, such as PyTorch and TensorFlow, applied to data generated through physical simulations. Participants will learn to implement and evaluate domain randomization strategies in various aerospace use cases, with the aim of improving the reliability and performance of AI systems. Prepares for roles such as data scientists, AI engineers, and machine learning specialists, fostering innovation in the aerospace industry.
Domain randomization course in aerospace AI
- Format: Hybrid
- Duration: 3 months
- Hours: 300 H
- Language: English / Spanish
- Registration date: 23-10-2026
- Start date: 12-12-2026
- Available places: 2
399 $
Competencies and results
What you will learn
1. Domain Randomization for Robust Training in Aerospace AI
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**What Will You Learn in “Domain Randomization for Robust Training in Aerospace AI”?**
Integrate AI models trained with Domain Randomization into flight control systems and aircraft design.
Use simulation tools and frameworks for implementing Domain Randomization (such as Unreal Engine, Unity).
Explore case studies and practical applications of Domain Randomization in the aerospace industry.
2. Mastering Randomization: Designing Wings and Propellers with Aerospace AI
- Exploring AI-driven aerodynamic design methodologies for wings and propellers.
Optimizing aerodynamic performance through randomization.
Generating innovative designs using AI-based optimization algorithms.
Studying the interaction between AI and fundamental aerodynamic principles.
Applying randomization techniques to explore the design space.
Evaluating the stability and control of AI-generated structures.
Developing predictive models for analyzing flight behavior.
Integrating manufacturing and performance constraints into the design process.
Simulating complex flows and performing sensitivity analysis using AI.
Application of genetic algorithms and other optimization techniques.
Analysis of the feasibility and efficiency of AI-generated designs.
Study of practical case studies of wing and propeller design using AI.
Learning specialized tools and software for AI-assisted aerodynamic design.
Consideration of environmental and sustainability factors in design.
Exploration of future trends in AI-driven aerospace design.
3. Comprehensive user-oriented design and validation (from modeling to manufacturing)
You will learn to integrate the entire product development process, from initial model conception to final validation, applying user-centered methodologies. You will develop skills in parametric design, ergonomics, simulation, sustainable materials, 3D visualization, and manufacturing management, ensuring efficient, safe solutions that meet current industry standards.
4. Data Generation and Simulation for Aerospace AI Development
- Understand the fundamental principles of data generation for artificial intelligence (AI) applied to aerospace.
- Learn about the computational simulation techniques used in the design and analysis of aerospace systems, including flight simulation and scenario simulation.
- Acquire knowledge of the specific tools and technologies used in data generation and simulation for the development of AI in the aerospace sector.
- Explore AI applications in aerospace, such as aircraft design, flight path optimization, and predictive maintenance.
- Study the challenges and ethical considerations related to the use of AI in aerospace.
- Analyze flap-lag-torsion, whirl flutter, and fatigue couplings.
- Dimension laminates in composites, unions and bonded joints with FE.
- Implement damage tolerance and NDT (UT/RT/thermography).
5. Implementation of Domain Randomization for AI Flight Control
- Fundamentals of Domain Randomization and its application in AI flight control.
- Reinforcement learning principles and optimization techniques for the design of intelligent controllers.
- Practical implementation of AI algorithms for flight management in simulated environments.
- Generation and adaptation of synthetic data (Domain Randomization) to improve controller robustness.
- Evaluation and analysis of the performance of AI-based flight controllers in different scenarios and conditions.
- Tools and techniques for the simulation and analysis of AI flight control.
- Development of flight simulation models and training environments.
- Design and optimization of AI-based flight controllers for aircraft.
- Safety considerations and validation of AI flight control systems.
6. Domain Randomization Strategies for AI Simulation of Aerial Systems
6. Domain Randomization Strategies for Air Systems Simulation with AI
- Understand the fundamentals of Domain Randomization (DR) in simulation.
- Explore the application of DR for the robustness of Artificial Intelligence (AI) models in air systems.
- Analyze the techniques for generating synthetic random data for AI training.
- Study the implementation of DR in different simulation environments (physical and virtual).
- Evaluate the impact of DR on the generalization and performance of AI models.
- Design strategies for parameter variation and environmental conditions in DR.
- Apply DR to specific problems in navigation, control, and
- Recognition in aerial systems.
- Integrate DR with reinforcement learning techniques for aerial system optimization.
- Analyze the evaluation and validation metrics of DR-trained models.
- Identify the challenges and limitations of DR and future research directions.
Who this program is for:
Domain randomization course in aerospace AI
- Graduates in Aerospace Engineering, Mechanical Engineering, Industrial Engineering, Automation Engineering, or related fields.
- Professionals in OEM rotorcraft/eVTOL, MRO, consulting, and technology centers.
- Flight Testing, certification, avionics, control, and dynamics seeking specialization.
- Regulators/authorities and UAM/eVTOL professionals requiring compliance skills.
Recommended requirements: foundation in aerodynamics and control and structures; ES/EN B2+/C1. We offer bridging tracks if you need them.
- Standards-driven curriculum: you will work with CS-27/CS-29, DO-160, DO-178C/DO-254, ARP4754A/ARP4761, ADS-33E-PRF from the first module.
- Accreditable laboratories (EN ISO/IEC 17025) with rotor bench, EMC/Lightning pre-compliance, HIL/SIL, vibrations/acoustics.
- Evidence-oriented TFM: safety case, test plan, compliance dossierand operational limits.
- Mentored by industry: teachers with experience in rotorcraft, tiltrotor, eVTOL/UAM and flight test.
- Flexible modality (hybrid/online), international cohorts and support from SEIUM Career Services.
- Ethics and security: safety-by-design approach, cyber-OT, DIH and compliance as pillars.
Module 1 — Introduction to Domain Randomization in Aerospace AI
1.1 Fundamental Concepts of AI and Machine Learning in the Aerospace Context
1.2 Introduction to Domain Randomization: Definition and Purpose
1.3 Benefits and Challenges of Domain Randomization in Aerospace AI
1.4 Initial Applications of Domain Randomization: Practical Examples
1.5 The Domain Randomization Process: Data Generation and Training
1.6 Understanding Robustness: Generalization and Adaptability of AI Models
1.7 Tools and Platforms for Implementing Domain Randomization
1.8 Metrics for Evaluating and Validating Models Trained with Domain Randomization
1.9 Case Study: Initial Application to an Aerospace Problem
1.10 Future Trends and Perspectives of Domain Randomization in Aerospace AI
2.2 Fundamentals of Randomization in Aeronautical Design
2.2 Generating Random Data for Wings and Propellers
2.3 Flow Simulation and Aerodynamic Analysis with Randomization
2.4 Wing Shape Optimization using Domain Randomization
2.5 Designing Efficient Propellers through Randomization
2.6 Integrating Domain Randomization into the Design Process
2.7 Performance Evaluation and Design Validation
2.8 Case Studies: Practical Applications in Design
2.9 Tools and Software for Randomization
2.20 Challenges and Future Trends in AI Design
3.3 Introduction to Domain Randomization and its application in Aerospace AI
3.2 Fundamentals of AI in aeronautical design and simulation
3.3 Domain Randomization Techniques: concepts and methodology
3.4 Generation of synthetic data for training AI models
3.5 Validation and evaluation of AI models with Domain Randomization
3.6 Practical examples and case studies in aerospace AI
2.3 Principles of wing and propeller design
2.2 Aerodynamic modeling and simulation with AI
2.3 Application of Domain Randomization in wing design
2.4 Application of Domain Randomization in propeller design
2.5 Optimization of aerodynamic performance with AI
2.6 Analysis of results and validation of designs
3.3 Optimization methodology based on Domain Randomization
3.2 Parametric aircraft design with AI
3.3 Simulation of different flight scenarios
3.4 Optimization of fuel efficiency
3.5 Design 3.6 System Integration and Overall Performance Analysis
4.3 Data Sources for AI Development in Aerospace
4.2 Computational Simulation Techniques
4.3 Creating Realistic Synthetic Datasets
4.4 Data Preprocessing and Cleaning Techniques
4.5 Using Flight Simulators for Data Generation
4.6 Data and Simulation Evaluation and Validation
5.3 Introduction to Flight Control
5.2 Designing AI-Based Flight Controllers
5.3 Implementing Domain Randomization for Controller Training
5.4 Adapting to Different Flight Conditions
5.5 Testing and Validating Controllers
5.6 Case Studies and Practical Examples
6.3 Modeling and Simulation of Complex Air Systems
6.2 Domain Randomization Strategies for Simulation
6.3 Simulation of Failures and Contingencies
6.4 Evaluating System Robustness 6.5 Analysis of System Response to Different Scenarios
6.6 Performance and Reliability Optimization
7.3 Design of Intelligent Air Traffic Controllers
7.2 Implementation of Domain Randomization in Controller Training
7.3 Controller Adaptability and Robustness
7.4 Integration with Navigation and Flight Management Systems
7.5 Testing and Evaluation of Intelligent Air Traffic Controllers
7.6 Applications and Future Perspectives
8.3 Analysis of Aerospace Structures with AI
8.2 Application of Domain Randomization in Structural Analysis
8.3 Strength and Durability Assessment
8.4 Structural Optimization and Weight Reduction
8.5 Fatigue and Service Life Analysis
8.6 Design of Advanced Structures with AI
4.4 Data Sources in the Aerospace Context
4.2 Simulation Methods for Data Generation
4.3 Simulation Tools and Platforms
4.4 Experimental Design and Sampling Strategies
4.5 Data Augmentation Techniques for AI
4.6 Simulation of Environments for Robust Training
4.7 Data Generation for Air Traffic Control Systems
4.8 Creation of Datasets for Structural Analysis
4.9 Validation and Verification of Simulated Data
4.40 Case Studies: Practical Applications of Data Generation in Aerospace AI
5.5 Introduction to Domain Randomization and its Application in Aerospace AI
5.5 Robustness Fundamentals in AI Model Training
5.3 Domain Randomization Techniques for Generating Varied Data
5.4 Setting Up Simulation Environments for Robust Training
5.5 Implementing Domain Randomization in Model Simulation
5.6 Evaluating and Analyzing the Robustness of the Trained Model
5.7 Case Studies: AI Training for Complex Scenarios
5.8 Challenges and Solutions in Applying Domain Randomization
5.9 Advanced Domain Randomization Strategies
5.50 Ethical and Safety Considerations in Aerospace AI
5.5 Introduction to Wing and Propeller Design with AI
5.5 Principles of Domain Randomization in Aerodynamic Design
5.3 Generating Random Data and Parameters for Wings and Propellers
5.4 Simulating and Evaluating AI-Generated Designs
5.5 Optimizing Aerodynamic Design Using Domain Randomization Randomization
5.6 Performance and Stability Analysis of Wings and Propellers
5.7 Integration of Design Constraints into the Optimization Process
5.8 Tools and Software for Aerodynamic Design with AI
5.9 Case Studies: Optimized Wing and Propeller Design
5.50 Future Trends in Aerodynamic Design with AI
3.5 Fundamentals of Aircraft Design Optimization
3.5 Application of Domain Randomization in Design Optimization
3.3 Definition of Design Variables and Constraints
3.4 Generation of Data and Varied Scenarios
3.5 Simulation and Evaluation of Aircraft Performance
3.6 Design Optimization for Different Objectives
3.7 Sensitivity and Robustness Analysis of the Optimized Design
3.8 Integration of External Factors into the Optimization Process
3.9 Case Studies: Aircraft Optimization with AI
3.50 Challenges and Future Perspectives in Design Optimization
4.5 Importance of Data Generation in AI Aerospace
4.5 Synthetic Data Generation Techniques
4.3 Simulation of Flight Environments and Scenarios
4.4 Experiment Design and Simulation Planning
4.5 Validation and Verification of Generated Data
4.6 Data Cleaning and Preprocessing
4.7 Selection of Relevant Features for Training
4.8 Tools and Platforms for Simulation and Data Generation
4.9 Case Studies: Data Generation for Different Applications
4.50 Future Trends in Data Generation and Simulation
5.5 Introduction to AI Flight Control
5.5 Application of Domain Randomization in Controller Design
5.3 Design of Robust Controllers Using Domain Randomization
5.4 Training and Validation of Controllers with Varied Data
5.5 Implementation of Controllers in Simulators
5.6 Testing and Evaluation of Controller Performance
5.7 Adaptation and Fine-Tuning of Controllers
5.8 Challenges and Solutions in AI Flight Control
5.9 Integration of Intelligent Controllers in Flight Systems
5.50 Future Perspectives and Trends in Flight Control with AI
6.5 Introduction to AI-Based Simulation of Air Systems
6.5 Designing Robust Simulations Using Domain Randomization
6.3 Generating Varied Simulation Scenarios
6.4 Simulating Different Flight Conditions and Failures
6.5 Evaluating System Performance and Stability
6.6 Analyzing Results and Optimizing System Design
6.7 Integrating AI into the Simulation Process
6.8 Tools and Software for Air Systems Simulation
6.9 Case Studies: AI-Based Simulation of Air Systems
6.50 Future Trends in Air Systems Simulation
7.5 Introduction to Intelligent Air Controllers
7.5 Designing and Developing Intelligent Controllers
7.3 Implementing Domain Randomization in Controller Design
7.4 Training and Validating AI Controllers
7.5 Testing and Evaluating Performance 7.6 Integration of Intelligent Controllers into Flight Systems
7.7 Safety and Performance Considerations
7.8 Case Studies: Intelligent Air Controllers
7.9 Challenges and Future Perspectives in Controller Development
7.50 Ethics and Responsibility in the Use of Intelligent Controllers
8.5 Introduction to Aerospace Structural Analysis
8.5 Application of Domain Randomization in Structural Analysis
8.3 Generation of Varied Data for Structural Analysis
8.4 Simulation and Evaluation of Structural Behavior
8.5 Optimization of Structural Design
8.6 Failure Analysis and Safety Design
8.7 Use of AI in Structural Analysis
8.8 Tools and Software for Structural Analysis
8.9 Case Studies: Structural Analysis with AI
8.50 Future Trends in Structural Analysis
6.6 Introduction to Air Simulation with Domain Randomization
6.2 Fundamentals of Domain Randomization for Simulation
6.3 Generation of Synthetic Data and Virtual Environments
6.4 Randomization Techniques Applied to Air Systems
6.5 Modeling Uncertainties and Variability in Simulation
6.6 Advanced Domain Randomization Strategies
6.7 Validation and Verification of Robust Simulations
6.8 Practical Applications in Aircraft Design and Control
6.9 Results Analysis and Decision Making
6.60 The Future of Air Simulation with Domain Randomization
- Hands-on methodology: test-before-you-trust, design reviews, failure analysis, compliance evidence.
- Software (depending on licenses/partners): MATLAB/Simulink, Python (NumPy/SciPy), OpenVSP, SU2/OpenFOAM, Nastran/Abaqus, AMESim/Modelica, acoustics tools, planning toolchains DO-178C.
- SEIUM Laboratories: scale rotor bench, vibrations/acoustics, EMC/Lightning pre-compliance, HIL/SIL for AFCS, data acquisition with strain gauging.
- Standards and compliance: EN 9100, 17025, ISO 27001, GDPR.
Capstone-type projects
Admissions, fees, and scholarships
- Profile: Background in Computer Engineering, Mathematics, Statistics, or related fields; practical experience in NLP and valued information retrieval systems.
- Documentation: Updated CV, academic transcript, SOP/statement of purpose, project examples or code (optional).
- Process: Application → Technical evaluation of profile and experience → Technical interview → Review of case studies → Final decision → Enrollment.
- Fees:
- Single payment: 10% discount.
- Payment in 3 installments: No fees; 30% upon registration + 2 equal monthly payments of the remaining 35%.
Monthly payment: available with a 7% commission on the total; annual review.
Scholarships: based on academic merit, financial need, and promoting inclusion; agreements with companies in the sector for partial or full scholarships.
See “Calendar & Calls for Applications,” “Scholarships & Grants,” and “Fees & Financing” in the SEIUM mega-menu.
Do you have any questions?
Our team is ready to help you. Contact us, and we will respond as soon as possible.
F. A. Q
Frequently asked questions
Yes, we have international certification
Yes: experimental models, real data, applied simulations, professional environments, real case studies.
It is not mandatory. We offer leveling tracks and tutoring.
Completely. It covers e-propulsion, integration, and emerging regulations (SC-VTOL).
Recommended. There are also internal challenges and consortiums.
Yes. Online/hybrid modality with planned labs and visa support (see “Visa & Residence”).