Computational Modeling and Simulation
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This curriculum teaches the computational and mathematical foundations of Computational Modeling and Simulation across applications. This includes both learning of models from data (Data Science, Machine Learning, Inference) as well as computer simulations of models. Specialization and knowledge transfer is acquired in the application-specific Tracks.The foundations in mathematics, computer science, and natural sciences with a strong emphasis on computer-based modeling and simulation form the prerequisites for application-specific specialization in one of the offered Tracks. The foundations include in particular: algorithmic and computatonal foundations, basic machine learning, data analysis, parallel programming, high-performance computing, numerical methods, probability and statistics, computer graphics and data visualization, stochastics, planning and analysis of computer experiments, and competence in literature studies in at least two application domains. 1. Track Computational Life Science:Computer modeling of biochemical networks, applied bioinformatics, modeling and simulation of biological systems and processes in space and time, statistical methods and experiment design, validation and verification of simulation results, dynamics of and on biological networks, mathematical biology, computational biophysics, scientific visualization in biology and medicine, particle methods, simulation of reaction networks, computer models in cognitive neuroscience, simulation methods for tissue biomechanics. 2. Track Computational Mathematics: Numerical analysis, numerically solving partial differential equations with finite element methods, scientific computing, computational methods in mathematical biology, mathematical modeling, numerics of partial differential equations, scientific programming, optimization methods, computational methods for multifield problems, computational statistics and Monte-Carlo methods.3. Track Visual Computing: Data visualization, algorithms for forward problems and inverse problems, user interface design, computer graphics, computer vision and image processing, information visualization, interactive media and multimedia, virtual reality, augmented reality, advanced machine learning and artificial intelligence.4. Track Computational Modeling in Energy Economics (partly taught in German!):Modeling and simulation of electricity markets, energy economy, simulation of national energy market systems, modeling environmental ressources and environment protection regulations, scientific computing, numerically solving partial differential equations.5. Track Computational Engineering: Computational fluid dynamics, simulation of multi-body dynamics, numerical methods for multifield problems, finite element methods for continuum mechanics, computer-aided design and optimization of technical systems.
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Dresden
Saxony
1069
Germany
- 2 years
- Full Time
- On Campus Learning
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