Diploma in Classical and Deep RL for Dynamic Systems
About us Diploma in Classical and Deep RL for Dynamic Systems
The Diploma in Classical and Deep Reinforcement Learning for Dynamic Systems explores the application of reinforcement learning (RL), in both its classical and deep forms, for the control and optimization of complex dynamic systems. It focuses on the development of algorithms and strategies that enable RL agents to learn to make optimal decisions in dynamic environments, including solving problems in systems control, robotics, and predictive modeling. The diploma program covers key concepts such as reward functions, Monte Carlo methods, dynamic programming, and deep neural networks (DNNs) applied to linear programming, preparing participants to implement advanced solutions in various engineering fields. The program includes the use of simulations and development environments to practice implementing linear programming algorithms in relevant case studies, such as mobile robots, autonomous navigation systems, and resource management. Emphasis is placed on understanding the specific challenges of dynamic systems, such as stability, uncertainty, and adaptability, using performance analysis and optimization tools. Graduates will be prepared to tackle projects in industries such as robotics, industrial automation, transportation, and energy, applying the knowledge acquired to improve the performance and efficiency of dynamic systems.
Target keywords (naturally occurring in the text): reinforcement learning, dynamic systems, systems control, robotics, RL algorithms, deep neural networks, optimization, simulation, autonomous control, diploma in RL.
Diploma in Classical and Deep RL for Dynamic Systems
- Format: Online
- Duration: 8 months
- Hours: 900 H
- Language: ES / EN
- Credits: 60 ECTS
- Registration date: 04-07-2026
- Strat date: 14-08-2026
- Available places: 9
979 $
Competencias y resultados
Qué aprenderás
1. Mastery of Modeling and Performance of Dynamic Systems with Classical and Deep Linear Relativity
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Fundamentals of dynamic systems applied to naval environments.
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Mathematical modeling of complex naval systems.
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Implementation of classical and deep reinforcement learning (RL) techniques for system optimization.
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Analysis and control of ship dynamics, including stability and maneuverability.
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Simulation of propulsion systems and their interaction with the hull.
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Application of RL in autonomous navigation and route optimization.
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Design and control of unmanned underwater vehicles (AUVs).
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Implementation of RL algorithms for fault detection and mitigation in naval systems.
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Optimization of fuel consumption and energy efficiency in ships.
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Data analysis and results visualization for decision-making in the naval sector.
2. Rotor Modeling and Performance Optimization with Reinforcement Learning
- Understand the fundamentals of rotor modeling, including computational fluid dynamics (CFD) and blade element theory (BEM).
- Apply reinforcement learning techniques to optimize rotor design, aiming to maximize efficiency and minimize vibrations.
- Design and simulate rotor control systems using reinforcement learning algorithms, including active vibration control.
- Evaluate the performance of optimized rotors through simulations and data analysis, considering different operating conditions.
- Develop rotor failure models and life analysis, including the consideration of fatigue and corrosion effects.
- Implement predictive maintenance strategies based on reinforcement learning to optimize inspection and repair intervals.
- Explore the applications of reinforcement learning in the optimization of wind turbines, helicopters, and other rotating systems.
- Use specialized software for modeling and simulation of rotors, as well as for the implementation of reinforcement learning algorithms.
Analyze the impact of design variables on rotor performance, such as blade profile, angle of attack distribution, and rotational speed.
Present the results of the analysis and optimization clearly and concisely, using data visualization techniques and technical reports.
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. Rotor Modeling and Optimization with Classical and Deep Relational Linearity for Dynamic Systems
Here is the requested content:
- Master the fundamentals of rotor modeling, including understanding and analyzing the complexities of dynamic systems.
- Apply advanced optimization techniques using classical and deep Reinforcement Learning (RL) algorithms to improve rotor performance and efficiency.
- Delve into the study of critical aeroelastic phenomena, such as flutter and vibrations, and develop strategies for their mitigation.
- Analyze flap-lag-torsion couplings, whirl flutter, and fatigue.
- Acquire skills in the structural design and analysis of rotors, including the dimensioning of critical components.
- Dimension laminates in composites, joints, and bonded joints. FE.
Learn to use state-of-the-art simulation and modeling software for rotor optimization and analysis.
Implement damage tolerance and non-destructive testing (NTT) (UT/RT/thermography).
Explore the practical applications of rotors in various industries, such as aerospace and wind energy.
Develop the ability to critically evaluate rotor performance and propose innovative solutions to improve their design and operation.
5. Implementation of Classical and Deep Relational Modeling for Rotor Modeling and Optimization in Dynamic Systems
- Fundamentals of Control Theory and Dynamic Systems applied to rotors.
- Principles of Classical and Deep Reinforcement Learning (RL): Markov Decision Processes (MDPs), value functions, and policies.
- Implementation of RL algorithms for rotor modeling and optimization: Q-learning, SARSA, Deep Q-Networks (DQN), gradient-based policies.
- Rotor modeling in simulated environments: creation of simulation models for rotor dynamic systems.
- Application of RL for rotor design optimization: adjustment of design parameters, vibration reduction, and improvement of aerodynamic efficiency.
- Analysis of Rotor stability and control using RL techniques.
- Evaluation of the performance and robustness of RL algorithms in different rotor operating scenarios.
- Practical implementation and experimentation: development of case studies and simulation of real-world scenarios.
- Integration of RL with simulation tools and rotor system analysis.
- Analysis of flap-lag-torsion, whirl flutter, and fatigue couplings.
- Dimensioning of laminates in composites, joints, and bonded joints with FE.
- Implementation of damage tolerance and NDT (UT/RT/thermography).
6. Optimization of Rotor Modeling Using Classical and Deep Relational Linearity in Dynamic Systems
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.
Para quien va dirigido nuestro:
Diploma in Classical and Deep RL for Dynamic Systems
- Graduates in Aerospace Engineering, Mechanical, Industrial, Automatics or similar.
- OEM rotorcraft/eVTOL professionals, MRO, consulting, technology centers.
- Flight Test, certification, avionics, control and dynamics who seek specialization.
- Regulators/authorities and UAM/eVTOL profiles that require compliance competencies.
- 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 Rotorcraft Dynamic Systems
1.1 eVTOL and UAM: Electric Propulsion, Multiple Rotors
1.2 Emerging Certification Requirements (SC-VTOL, Special Conditions)
1.3 Energy and Thermal in E-Propulsion (Batteries/Inverters)
1.4 Design for Maintainability and Modular Swaps
1.5 LCA/LCC in Rotorcraft and eVTOL (Footprint and Cost)
1.6 Operations & Vertiports: Airspace Integration
1.7 Data & Digital Thread: MBSE/PLM for Change Control
1.8 Tech Risk and Readiness: TRL/CRL/SRL
1.9 IP, Certifications, and Time-to-Market
1.10 Case Clinic: Go/No-Go with Risk Matrix
2.2 Introduction to rotor dynamics: fundamentals and key concepts.
2.2 Mathematical modeling of rotors: equations and simplifications.
2.3 Reinforcement Learning (RL): introduction and basic concepts.
2.4 Classical RL: algorithms and applications in rotor modeling.
2.5 Deep RL: neural networks and their use in optimization.
2.6 Implementing RL for rotor modeling.
2.7 Optimizing rotor performance using RL.
2.8 Analysis and evaluation of results: metrics and validation.
2.9 Case studies: applying RL in different scenarios.
2.20 Future trends and perspectives of reinforcement learning in rotor optimization.
3.3 Introduction to Reinforcement Learning: Key Concepts
3.2 Classical Reinforcement Learning: Algorithms and Applications in Rotor Systems
3.3 Deep Reinforcement Learning: Neural Networks for Rotor Modeling and Control
3.4 Designing Rotor Models for Reinforcement Learning: Simulation and Environment
3.5 Performance Optimization: Reinforcement Learning Strategies for Rotors
3.6 Hyperparameter Tuning: Maximizing Performance with Reinforcement Learning
3.7 Results Analysis: Metrics and Optimization Evaluation
3.8 Practical Implementation: Developing a Rotor Simulator with Reinforcement Learning
3.9 Case Studies: Applications of Reinforcement Learning in Rotor Design
3.30 Future Trends: Reinforcement Learning in Rotor System Innovation
4.4 Fundamentals of Classical and Deep Reinforcement Learning (RL) for Dynamic Systems
4.2 Mathematical Modeling of Rotors: Equations and Key Parameters
4.3 Implementation of Simulation Environments for Rotorcraft
4.4 Design of Reward Functions for Performance Optimization
4.5 Classical RL Algorithms: Q-Learning, SARSA, and Their Applications
4.6 Deep RL Algorithms: Neural Networks and Architectures for Control
4.7 Performance Optimization: RL-Based Control Strategies
4.8 Results Analysis: Performance Metrics and Policy Evaluation
4.9 Fine-Tuning and Hyperparameter Tuning in RL
4.40 Case Studies: Applications of RL in Rotorcraft Design and Control
5.5 Introduction to Reinforcement Learning Implementation in Modeling and Optimization
5.5 ​​Review of Key Reinforcement Learning (RL) Concepts
5.3 Modeling Dynamic Systems for Rotors
5.4 Implementing Classical RL in Modeling and Optimization
5.5 ​​Implementing Deep RL in Modeling and Optimization
5.6 Comparative Analysis: Classical RL vs. Deep RL
5.7 Specific Applications: Performance Optimization
5.8 Specific Applications: Controller Design
5.9 Case Studies: Real-World Applications
5.50 Challenges and Future of Reinforcement Learning
5.50
6.6 Introduction to Dynamic Systems: Key Concepts
6.2 Rotorcraft Flight Dynamics: Fundamental Principles
6.3 Main Components of Rotorcraft: Overview
6.4 Mathematical Modeling of Rotorcraft: Approximations and Simplifications
6.5 Simulation Software and Tools: Introduction and Basic Use
6.6 Rotorcraft Stability and Control: Basic Concepts
6.7 Introduction to Reinforcement Learning (RL) and Its Application in Rotorcraft
2.6 Aerodynamic Modeling of Rotors: Blade Element Theory
2.2 Modeling of Actuators and Control Systems: Equations and Simulation
2.3 Implementing Classical RL: Q-Learning, SARSA
2.4 Designing Simulation Environments for RL: Reward and Penalty
2.5 Modeling and Simulating Rotorcraft Dynamic Systems with Classical RL
2.6 Analysis of Results and Initial Performance Optimization
2.7 Hyperparameter Tuning in Classical RL
3.6 Performance Optimization Strategies: Objective Functions
3.2 Implementing Algorithms RL-based optimization
3.3 Applying RL to rotor optimization: practical examples
3.4 Designing rewards for optimization: stability and performance
3.5 Comparing classic and deep RL in optimization
3.6 Analyzing the impact of optimization on energy efficiency
3.7 Evaluating the robustness and generalizability of optimized policies
4.6 Complete rotorcraft modeling: integrating subsystems
4.2 Implementing deep RL: neural networks and value functions
4.3 Designing complex simulation environments for deep RL
4.4 Training techniques in deep RL: repeated experimentation, objectives
4.5 Optimizing performance with deep RL: comparative analysis
4.6 Evaluating stability and convergence in deep RL
4.7 Validating results and sensitivity analysis
5.6 Software architecture for RL implementation
5.2 Integrating RL into existing rotorcraft simulators
5.3 Designing interfaces and data flow
5.4 Implementing algorithms RL: TensorFlow, PyTorch
5.5 Testing and Validation of the RL Implementation
5.6 Analysis of Results and Fine-Tuning of the Implementation
5.7 Development of a Practical Simulation and Optimization Case Study
6.6 Design of Rotorcraft Models Optimized with RL
6.2 Parametric Modeling Techniques: Variables and Constraints
6.3 Fine-Tuning of RL Algorithms for Modeling
6.4 Analysis of Model Complexity: Trade-offs
6.5 Evaluation of the Performance of the Optimized Model
6.6 Model Validation and Verification Methods
6.7 Case Studies and Comparative Results
7.6 Sensitivity and Stability Analysis in Rotor Systems
7.2 Failure Mode Analysis and Mitigation
7.3 Design of Robust Controllers with RL: Adaptation
7.4 Evaluation of System Performance with RL
7.5 Risk Identification and Mitigation
7.6 Analysis of Results and Conclusions
7.7 Case Studies: Practical Applications
8.6 Selection of RL Algorithms: Classification
8.2 Design of Modeling Systems with RL
8.3 Implementation of Optimization Policies
8.4 Integration of Modeling and Optimization
8.5 Analysis of Results and Conclusions
8.6 Case Studies
8.7 Challenges and Future Perspectives
7.7 Fundamentals of Classical and Deep Reinforcement Learning (RL) in Dynamic Systems
7.2 Rotor Modeling: Principles and Simulation Techniques
7.3 Implementation of Classical RL Algorithms for Rotor Optimization
7.4 Design of Deep RL Agents for Rotor Modeling and Control
7.7 Integration of RL in the Design and Optimization Process of Dynamic Systems
7.6 Data Analysis and Performance Evaluation of RL Algorithms
7.7 Hardware and Software Considerations for Real-Time RL Implementation
7.8 Case Studies: Practical Applications of RL in Rotor Modeling and Optimization
7.9 Challenges and Limitations of RL Implementation in Dynamic Systems
7.70 Future of RL in Rotor Modeling and Optimization
7.9
8.8 Introduction to Reinforcement Learning (RL)
8.8 Modeling Dynamic Systems for Rotorcraft
8.3 Classical RL for Rotor Modeling and Optimization
8.4 Advanced RL for Rotor Modeling and Optimization
8.5 Practical Implementation: RL in Rotorcraft Simulation
8.6 Analysis of Results and Tuning of RL Models
8.7 Advanced Optimization Strategies with RL
8.8 Integrating RL into Rotorcraft Design
8.8 Case Studies: Real-World Applications of RL
8.80 Future Trends and Emerging Applications of RL
8.9
9.9 Introduction to Dynamic Systems and Rotorcraft
9.9 Principles of Dynamic Systems Modeling
9.3 Fundamentals of Rotor Aerodynamics
9.4 Modeling Rotor Blades and Their Components
9.5 Rotorcraft Simulation and Analysis Methods
9.6 Key Variables and Parameters in Modeling
9.7 Introduction to Reinforcement Learning (RL)
9.8 Concepts of Classical and Deep RL
9.9 Applications of Modeling in the Rotorcraft Industry
9.90 Examples of Rotorcraft Systems
9.9 Introduction to Reinforcement Learning (RL)
9.9 The RL Process
9.3 Classical RL Methods for Modeling
9.4 Deep RL Techniques
9.5 Designing Reward Functions
9.6 Implementing RL Algorithms
9.7 Modeling Rotors with RL
9.8 Considerations for Stability and Convergence
9.9 Data Analysis and Performance Evaluation
9.90 Case Studies of Rotor Modeling with RL
3.9 Optimization Fundamentals
3.9 Optimization Strategies
3.3 Performance Optimization with RL
3.4 Designing Reward Functions for Optimization
3.5 Classical RL Methods in Optimization
3.6 Deep RL Techniques in Optimization
3.7 Applying RL to Rotor Optimization
3.8 Parameter Tuning and Algorithm Fine-Tuning
3.9 Results Analysis and Optimization Validation
3.90 Practical Examples of Rotor Optimization
4.9 Integrating Modeling and Optimization with RL
4.9 Designing Simulation Environments with RL
4.3 Developing Dynamic System Models for RL
4.4 Implementing Classical RL in Modeling and Optimization
4.5 Applying Deep RL in Modeling and Optimization
4.6 Selecting RL Algorithms
4.7 Analyzing Model Results and Optimization
4.8 Model Validation and Verification
4.9 Scalability and Performance Considerations
4.90 Practical Examples of Modeling and Optimization
5.9 Selection of Platforms and Tools for Implementation
5.9 Implementation of Classical RL
5.3 Implementation of Deep RL
5.4 System Architecture Design
5.5 ​​Algorithm Testing and Debugging
5.6 Integration with Simulation Software
5.7 Hyperparameter Tuning and Performance Optimization
5.8 Results Analysis and Validation
5.9 System Scalability and Adaptability
5.90 Implementation Case Studies
6.9 Review of Rotorcraft Modeling
6.9 Design of Experiments to Optimize the Modeling
6.3 Application of RL Algorithms for Model Optimization
6.4 Design of Reward Functions for Optimization
6.5 Optimization Methods and Model Fine-Tuning
6.6 Validation and Verification of the Optimized Model
6.7 Comparison of Different Modeling Approaches
6.8 Performance Optimization and Computational Efficiency
6.9 Sensitivity Analysis and Risk Assessment
6.90 Industry Application Examples
7.9 Review of Systems Rotors
7.9 Performance and Efficiency Analysis
7.3 Application of RL for the Analysis of Dynamic Systems
7.4 Design of Reward Functions for Analysis
7.5 Analysis of Data and Model Results
7.6 Identification of Areas for Improvement
7.7 Optimization of System Parameters
7.8 Simulation and Validation of Results
7.9 Case Studies and Applications
7.90 Risk Analysis and Mitigation
8.9 Modeling and Simulation of Rotors with RL
8.9 Implementation of RL Algorithms
8.3 Design of Reward Functions
8.4 Parameter and Performance Optimization
8.5 Analysis of Results and Validation
8.6 Integration with Dynamic Systems
8.7 Case Studies and Applications
8.8 Scalability and Adaptability
8.9 Performance Considerations
8.90 Future Implications and Conclusions
1. Fundamentals of Classical and Deep Relational Modeling for Rotor Modeling
2. Dynamic Rotor Modeling with Relational Modeling
3. Rotor Performance Optimization with Classical Relational Modeling
4. Rotor Performance Optimization with Deep Relational Modeling
5. Practical Implementation of Relational Modeling in Rotor Systems
6. Integration of Classical and Deep Relational Modeling for Rotor Optimization
7. Analysis and Evaluation of Rotor Performance with Relational Modeling
8. Model Validation and Verification with Relational Modeling in Dynamic Systems
9. Scalability and Generalization of Relational Modeling for Rotors
10. Case Studies and Advanced Applications in Industry
10. Case Studies and Advanced Applications in Industry
- 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.
Proyectos tipo capstones
Admisiones, tasas y becas
- 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.
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