Course on Simulating Urban Scenarios with AI
About our
The Urban Scenario Simulation with AI course explores the use of artificial intelligence to model and analyze complex urban environments. It focuses on the application of machine learning (ML) and agent-based simulation techniques to understand and predict phenomena such as vehicle traffic, population distribution, and environmental impact. data visualization and spatial analysis tools are covered for decision-making in urban planning, including the study of smart infrastructure and resource optimization. The course trains participants in the development of predictive models and the evaluation of urban strategies, with a focus on sustainability and energy efficiency. The use of urban simulation platforms and big data analysis is encouraged to better understand the dynamics of cities. Practical case studies from different urban environments are explored, and AI algorithms are used to optimize urban design and resource management. The training enables the development of skills in 3D modeling and the creation of simulated scenarios for the evaluation of public policies, preparing participants for roles in urban planning, data management, and technology consulting.
Course on Simulating Urban Scenarios with AI
- Format: Online
- Duration: 3 months
- Hours: 300 H
- Language: English / Spanish
- Registration date: 23-10-2026
- Start date: 12-12-2026
- Available places: 2
320 $
Competencies and results
What you will learn
1. Mastery of Smart City Simulation: Analysis and Strategies
-
Identify and apply smart city simulation methodologies for the comprehensive analysis of urban environments.
-
Use specialized software for the modeling and simulation of complex urban systems, including traffic, energy, water, and waste models.
-
Evaluate the impact of urban design decisions on the efficiency, sustainability, and resilience of cities.
-
Analyze geospatial and sensor data for evidence-based decision-making, optimizing resources and improving quality of life.
-
Develop strategies for the implementation of smart city solutions, considering aspects such as infrastructure, governance, and citizen participation.
-
Understand the principles of data analysis and artificial intelligence applied to smart city simulation for prediction and optimization.
-
Apply data visualization techniques to effectively communicate the results of simulations and analyses to different audiences.
Evaluate the feasibility and economic impact of smart city projects, considering the associated costs, benefits, and risks.
Analyze case studies of successful smart cities, identifying best practices and lessons learned.
Develop skills for interdisciplinary collaboration and project management in the field of smart urban simulation.
2. Urban AI: Simulations, Strategies and Scenario Optimization
- Explore the fundamentals of Artificial Intelligence (AI) applied to the urban environment.
- Master the use of advanced simulations to model and predict complex urban behaviors.
- Develop effective strategies for urban planning based on data and predictive analytics.
- Optimize urban scenarios using AI algorithms, including traffic management, energy efficiency, and public safety.
- Learn to integrate data from diverse sources (sensors, images, social networks) for a more robust urban AI.
- Study the impact of AI on the sustainability and resilience of cities in the face of challenges such as climate change.
- Analyze real-world case studies of urban AI implementation in different cities. world.
Become familiar with the most widely used software tools and platforms in the field of urban AI.
Understand the ethical and social implications of AI in the urban context.
Design and develop innovative AI-based solutions for specific urban problems.
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. Development of Smart Urban Scenarios: Design and Simulation
4. Development of Smart Urban Scenarios: Design and Simulation
Explore the application of simulation technologies for creating digital models of cities.
Learn about the use of geospatial data and its integration into urban simulations.
Master the design of 3D models of buildings, infrastructure, and urban elements.
Study the simulation of traffic, pedestrian, and public transportation flows.
Analyze the impact of energy, water, and waste in urban environments.
Understand the design and simulation of smart grids.
Explore the modeling and simulation of urban communication and connectivity networks.
Apply data analysis and visualization tools to interpret simulation results.
Design scenarios to evaluate the impact of urban planning decisions and public policies.
5. Simulation of Urban Scenarios with AI: Design, Implementation and Analysis
5. Urban Scenario Simulation with AI: Design, Implementation, and Analysis
- Identify and understand the key elements that comprise an urban scenario for AI simulation.
- Design urban simulation models using AI architectures, considering relevant variables (traffic, pedestrians, weather, etc.).
- Implement AI algorithms (machine learning, neural networks) to simulate the behavior of agents and systems in the urban environment.
- Analyze the simulation results, evaluating the impact of different scenarios and strategies.
- Use visualization tools to represent the simulation results and facilitate understanding.
- Optimize urban simulation models to improve efficiency and accuracy.
- Integrate real-world data (sensors, historical data) to improve the validity of the simulations.
- Develop practical applications of urban simulation, such as transportation planning, emergency management, and infrastructure optimization.
- Explore the ethical and social considerations related to the use of AI in urban simulation.
- Learn to effectively communicate simulation results to both technical and non-technical audiences.
6. AI in Urban Environments: Simulation, Development and Evaluation
- Identify and understand the fundamentals of Artificial Intelligence (AI) and its application in the urban context.
- Simulate complex urban scenarios using AI tools and techniques, including predictive modeling and data analysis.
- Develop AI applications to address specific urban challenges, such as traffic management, urban planning, and resource optimization.
- Evaluate the performance and effectiveness of AI solutions implemented in urban environments, using relevant metrics and analysis.
- Explore the ethical and social considerations related to the use of AI in cities.
- Acquire knowledge of the latest trends and advances in the field of AI applied to urban environments.
- Learn to use AI development platforms and tools, as well as as programming languages relevant to urban data analysis.
Understand the interaction between AI and other emerging technologies, such as the Internet of Things (IoT) and cloud computing, in the urban context.
Analyze case studies of successful AI implementations in cities around the world, identifying lessons learned and best practices.
Develop practical skills to design, implement, and evaluate AI solutions that improve the quality of life in urban environments.
Who this program is for:
Course on Simulating Urban Scenarios with AI
- Professionals and students interested in the analysis and simulation of urban environments using artificial intelligence.
- Engineers and technicians from various disciplines:
- Aerospace Engineering
- Mechanical Engineering
- Industrial Engineering
- Automation Engineering
- Related fields
- Personnel from companies and organizations involved in the industry:
- Original Wing/eVTOL Aircraft Manufacturers (OEMs)
- Maintenance, Repair, and Operations (MRO) Companies
- Consulting Firms
- Technology and Research Centers
- Specialists in specific areas of the sector Aeronautical:
Flight Test Personnel
Aeronautical Certification Experts
Avionics Professionals
Flight Dynamics and Control Specialists
UAM/eVTOL Industry Regulators, Authorities, and Profiles:
Regulatory Body Personnel
Compliance Professionals
Recommended Requirements: Familiarity with aerodynamics, control systems, and structures. A B2+/C1 level of Spanish/English is recommended. Bridging tracks are available to address any gaps.
- 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.
1.1 Definition of Smart City Simulation: Key Concepts and Applications
1.2 Importance of AI in Urban Modeling and Analysis
1.3 Tools and Platforms for Urban Simulation
1.4 Collection and Preparation of Urban Data: Sources and Formats
1.5 Introduction to AI Algorithms for Simulation
1.6 Design of Simulated Urban Scenarios
1.7 Analysis of Simulation Results and Data Visualization
1.8 Introduction to Urban Model Optimization
1.9 Case Studies: Practical Examples of Urban Simulation with AI
1.10 Future Trends in Smart City Simulation
2.2 Introduction to Smart Cities and AI
2.2 Designing Urban Infrastructure for Simulation
2.3 Collecting and Preparing Urban Data
2.4 Urban Simulation Tools and Their Functionalities
2.5 Designing Initial Urban Scenarios
2.6 Principles of Artificial Intelligence Applied to Simulation
2.7 User Interface Design and Results Visualization
2.8 Ethics and Privacy in Designing Urban Environments with AI
2.9 Case Studies: Examples of Smart Urban Design
2.20 The Future of Urban Design with AI
2.2 Analysis of Geospatial and Urban Data
2.2 Introduction to Smart Urban Simulations
2.3 Metrics and KPIs for Evaluating Simulations
2.4 Analysis of Urban Mobility
2.5 Analysis of Energy Consumption and Efficiency
2.6 Analysis of Waste Management
2.7 Techniques for Visualizing Simulation Results
2.8 Interpreting Results and Decision Making
2.9 Integration of data from diverse sources
2.20 Application of predictive models
3.2 AI fundamentals: machine learning and neural networks
3.2 Creation of predictive models for urban traffic
3.3 Modeling energy demand and consumption
3.4 Design of optimization models for resource management
3.5 Implementation of agent-based simulation models
3.6 Use of simulation tools and specialized software
3.7 Integration of AI models into simulation platforms
3.8 Development of models for environmental impact simulation
3.9 Validation and calibration of models
3.20 Practical cases of AI model creation
4.2 AI-based optimization techniques
4.2 Optimization of traffic flow and mobility
4.3 Optimization of energy consumption and efficiency
4.4 Evaluation of the quality of results
4.5 Sensitivity analysis and scenarios
4.6 Optimization of resource and waste management
4.7 Performance Evaluation Tools and Metrics
4.8 Validation of Models and Simulation Results
4.9 Parameter Adjustment and Model Calibration
4.20 Application of Simulation in Decision-Making
5.2 Conceptual Design of Urban Scenarios with AI
5.2 Design of Urban Simulation Models
5.3 Implementation of Simulation Models
5.4 User Interface Design and Results Visualization
5.5 Urban Transport Simulation
5.6 Simulation of Energy and Resource Use
5.7 Urban Design Optimization
5.8 Scenario and Alternative Analysis
5.9 Integration with Geographic Information Systems (GIS)
5.20 Case Studies of Urban Design with AI
6.2 Implementation of AI Models on Simulation Platforms
6.2 Real-Time Data Integration
6.3 Implementation of Optimization Algorithms
6.4 Analysis of Simulation Results
6.5 Evaluation of the Impact of Implemented Strategies
6.6 Efficiency Analysis Energy
6.7 Mobility and Traffic Analysis
6.8 Waste Management Analysis
6.9 Identifying Areas for Improvement
6.20 Presenting Results and Conclusions
7.2 AI Applications in Urban Planning
7.2 Transport and Mobility Simulation
7.3 Energy Consumption Simulation
7.4 Waste and Resource Management
7.5 Developing Simulation Models
7.6 Analyzing Urban Scenarios
7.7 Optimizing Urban Infrastructure
7.8 Case Studies: Smart Cities and Success Stories
7.9 Ethical and Social Considerations
7.20 The Future of Urban Simulation with AI
8.2 Advanced AI Strategies in the Urban Context
8.2 Using AI for Traffic Optimization
8.3 AI for Energy and Resource Management
8.4 AI for Security and Emergency Response
8.5 Developing Digital Twins
8.6 Artificial Intelligence in Urban Design
8.7 AI for Sustainability and the Environment
8.8 Integrating AI Systems into Infrastructure
8.9 Challenges and Opportunities of Smart Cities
8.20 The Future of Smart Cities with AI
3.3 Introduction to Creating Urban Simulations with AI
3.2 Selecting Simulation Tools and Platforms
3.3 Collecting and Preparing Urban Data
3.4 Designing the Urban Simulation Model
3.5 Implementing AI in the Simulation
3.6 Running and Analyzing Simulations
3.7 Optimizing Urban Scenarios with AI
3.8 Visualizing and Presenting Results
3.9 Practical Applications of Simulations
3.30 Future Trends in Urban Simulation with AI
4.4 Introduction to Artificial Intelligence in Urban Planning
4.2 Fundamentals of Urban Simulation: Modeling and Design
4.3 Data Collection and Analysis for Urban Environments
4.4 Design of Smart Urban Scenarios
4.5 Implementation of Simulation Models with AI
4.6 Analysis and Evaluation of Simulation Results
4.7 Optimization of Urban Scenarios with AI
4.8 Tools and Software for Smart Urban Simulation
4.9 Practical Applications and Case Studies
4.40 The Future of Smart Urban Planning and AI
5.5 Introduction to Smart City Simulation: Concepts and Fundamentals
5.5 Collection and Analysis of Urban Data: Sources and Methodologies
5.3 City Modeling and Representation: Geometry and Entities
5.4 Spatial Data Analysis: Tools and Techniques
5.5 Simulation Strategies: Types and Applications
5.6 Design of Simulation Experiments: Variables and Parameters
5.7 Evaluation of Results: Metrics and Key Indicators
5.8 Case Studies: Real-World Applications of Urban Simulation
5.9 Simulation Tools: Software and Platforms
5.50 Future Trends: The Future of Smart City Simulation
5.5 Introduction to Artificial Intelligence in Urban Environments
5.5 Machine Learning Algorithms for Urban Simulation
5.3 AI-Based Optimization: Techniques and Applications
5.4 Designing Simulations with AI: Integration and Workflows
5.5 Urban Scenario Analysis: Impact of AI
5.6 Modeling Human Behavior with AI
5.7 Urban Transport Optimization: AI in Mobility
5.8 AI for Energy and Resource Management
5.9 Ethics and Social Considerations of Urban AI
5.50 Case Studies: Successful Urban AI Implementations
3.5 Selection of Tools and Platforms for AI Simulation
3.5 Creating Urban Models: Techniques and Best Practices
3.3 AI Integration: Incorporating Algorithms and Models
3.4 Experiment Design: Defining Scenarios and Variables
3.5 Simulation Execution and Monitoring
3.6 Results Analysis: Interpretation and Visualization
3.7 Scenario Optimization: Parameter Tuning with AI
3.8 Simulation Validation and Verification
3.9 Case Studies: In-Depth Application Analysis
3.50 Presentation of Findings and Recommendations
4.5 Definition of Smart City Scenarios: Objectives and Requirements
4.5 Conceptual Scenario Design: Methodologies and Approaches
4.3 Modeling and Simulation of Urban Infrastructure
4.4 Simulation of User Behavior
4.5 Real-Time Data Integration
4.6 Impact Analysis of Design Decisions
4.7 Design Optimization: Iteration and Continuous Improvement
4.8 Simulation of Urban Mobility
4.9 Presentation and Communication of Design Results
4.50 Case Studies: Smart City Design and Simulation
5.5 Integration of AI in Urban Scenario Design
5.5 Implementation of AI Algorithms for Simulation
5.3 Configuration and Adjustment of Simulation Parameters
5.4 Simulation Execution and Data Collection
5.5 Results Analysis: Scenario Evaluation
5.6 Scenario Optimization: Application of AI Techniques
5.7 Visualization and Presentation of Results
5.8 Model Validation and Verification
5.9 Scalability and Performance Considerations
5.50 Case Studies: Implementations and Analysis
6.5 Introduction to AI in the Urban Environment
6.5 Modeling Urban Systems with AI
6.3 Development of AI Algorithms and Models for Simulation
6.4 Implementation of AI Models in Urban Environments
6.5 Evaluation of AI Model Performance
6.6 Evaluation Metrics: Accuracy, Robustness, and Generalizability
6.7 Design of Experiments to Evaluate the Impact of AI
6.8 Sensitivity Analysis and Results Validation
6.9 Ethics and Biases in Urban AI
6.50 Case Studies: Evaluating AI in Practice
7.5 Developing AI Applications for Urban Environments
7.5 Data Integration: Sources and Formats
7.3 User Interface and Experience Design
7.4 Simulating Urban Scenarios with AI
7.5 Analysis and Evaluation of Results
7.6 AI Applications in Urban Management
7.7 AI in Mobility: Transport Optimization
7.8 AI in Energy: Management and Efficiency
7.9 Practical Applications and Case Studies
7.50 Future Trends and Challenges of Urban AI
8.5 Designing Intelligent Urban Simulations with AI
8.5 Data Analysis: Preparation and Preprocessing
8.3 Selecting and Applying AI Algorithms
8.4 Optimization with AI: Parameter and Scenario Tuning
8.5 Running and Analyzing Simulations
8.6 Visualizing Results and Communication
8.7 Impact Assessment and Decision Making
8.8 Implementation and Scalability
8.9 Case Studies: Real-World Applications
8.50 Future Trends and Challenges in AI-Powered Urban Simulation
6.6 Introduction to Smart City Simulation
6.2 Conceptual Framework and Analysis Methodologies
6.3 Collection and Analysis of Urban Data
6.4 Modeling and Simulation of Urban Systems
6.5 Data Analysis and Prediction Strategies
6.6 Visualization and Communication of Results
6.7 Case Studies: Real-World Applications
6.8 Urban Simulation Tools and Software
6.9 Challenges and Future Trends
6.60 Ethics and Responsibility in Urban Simulation
2.6 Fundamentals of Artificial Intelligence in Urban Environments
2.2 AI Applications in Urban Simulation
2.3 Optimization Techniques for Urban Scenarios
2.4 Implementation of AI Algorithms
2.5 Evaluation of Models and Results
2.6 Design of Experiments and Sensitivity Analysis
2.7 Simulation of Urban Flows with AI
2.8 Optimization of Urban Resources and Services
2.9 Data Integration and AI Models
2.60 Case Studies and Optimization Studies
3.6 Introduction to the Creation of Smart Urban Scenarios
3.2 Data Selection and Preparation for Simulation
3.3 Design of AI Simulation Models
3.4 Implementation of AI Algorithms and Techniques
3.5 Analysis and Validation of Simulation Results
3.6 Parameter and Scenario Optimization
3.7 Sensitivity Analysis and What-If Scenarios
3.8 Simulation Tools and Platforms
3.9 Applications in Urban Planning and Management
3.60 Case Studies of Creation and Optimization
4.6 Introduction to the Design of Smart Urban Environments
4.2 Urban Design and Planning Considerations
4.3 Modeling and Simulation of Complex Urban Systems
4.4 Data Integration and Tools Simulation
4.5 Results Analysis and Decision Making
4.6 What-If Scenario Design and Evaluation
4.7 Urban Transport and Mobility Simulation
4.8 Urban Energy and Sustainability Modeling
4.9 Smart and Sustainable City Design
4.60 Design and Simulation Case Studies
5.6 Design of AI Simulation Models
5.2 Implementation of AI Algorithms and Techniques
5.3 Data and Information Source Integration
5.4 Model Validation and Verification
5.5 Results Analysis and Optimization
5.6 Experimental Design and Sensitivity Analysis
5.7 Intelligent Transportation System Simulation
5.8 Implementation of AI Solutions in Real-World Scenarios
5.9 Impact Assessment of Decisions
5.60 Design, Implementation, and Analysis Case Studies
6.6 Introduction to AI in Environments Urban
6.2 Developing AI Models for Urban Simulation
6.3 Integrating Data and Systems in Urban Environments
6.4 Simulating and Evaluating Urban Scenarios
6.5 Data Analysis and Performance Metrics
6.6 Evaluating the Impact of Decisions
6.7 Implementing AI Solutions in Practice
6.8 Challenges and Opportunities of Urban AI
6.9 Ethical and Social Aspects of Urban AI
6.60 Case Studies and Real-World Applications
7.6 Introduction to Developing Urban Scenarios with AI
7.2 Data Analysis and Modeling of Urban Systems
7.3 Designing Simulation Models with AI
7.4 Implementing AI Algorithms and Techniques
7.5 Analyzing Results and Optimization
7.6 AI Applications in Urban Mobility
7.7 AI Applications in Resource Management
7.8 Applications of AI in Urban Sustainability
7.9 Case Studies and Practical Examples
7.60 Trends and Future of Urban AI
8.6 Introduction to Intelligent Urban Simulation with AI
8.2 Designing Simulation Models with AI
8.3 Data Analysis and Preparation
8.4 Implementing AI Algorithms and Techniques
8.5 Optimizing Urban Scenarios
8.6 Results Analysis and Validation
8.7 Designing Experiments and Sensitivity Analysis
8.8 Integrating Data and Simulation Models
8.9 Case Studies: Design, Analysis, and Optimization
8.60 Urban Simulation Tools and Platforms
- 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”).