Theoretical aspects and fundamentals
- Mathematical theory and formulation of inverse and optimization problems
- Neural meta-modelling
- Regularization techniques
- (Model) order reduction
- Identification problems
- Sensitivity analysis
Algorithms
- Machine learning techniques for optimization and inverse problems
- Reconstruction techniques
- Deterministic, stochastic and hybrid techniques
- Multi-objective and multi-level optimization
- Heuristic approaches
- Design of experiments
- Constraints
- Robust optimization under uncertainty
- Objective functions and direct problems
- Numerical efficiency
- Numerical problems
Applications
- Optimal energy management
- Biomedical engineering
- Control systems
- Coupled problems
- Electrical machines
- Industrial and biomedical tomography
- Information and communication systems
- Large scale systems
- Mechatronics
- Micro- and nanosystems
- Non-destructive evaluation
- Design optimization
- Sensors and actuators
- Smart applications
- Transportation and mobility
- High frequency and antenna design
Software methodologies
- Parallel and distributed computing, GPU
- Soft computing and artificial intelligence
