2.096/6.336/16.910  Introduction to Numerical Simulation

Many complex systems found in nature/society and studied by social/economical/physical scientists (e.g. the human cardiovascular system, the brain neural network, biological systems, the geophysical network of oil/water/gas reservoirs, social networks), or developed by engineers (e.g. labs on chips, iPads, magnetic resonance scanners, nationwide electrical/gas/oil transportation network, supply chains, currency/stock markets, buildings/automotive/aircraft frames) can be viewed as large collections of interconnected dynamical system components. The performance of such complex systems typically depend on complicated constitutive and conservation relations between components, as well as on random uncertainties.
The goal of this course is to provide students with a working hands-on knowledge of the state of the art in modeling, simulation, model order reduction and uncertainty quantification techniques. Examples will be drawn from a large variety of complex and multi-disciplinary dynamical systems, as well as from student proposed applications, helping them with their own research projects in different engineering and science disciplines that deal with complex systems.
Students will be working in small teams on a course-long project involving modeling and simulation of a complex system either self-proposed from their own field of research, or chosen from a few examples developed in class. Time in class will include short lectures interleaved by numerous interactive and hands-on activities coordinated by the instructor and supporting the self-proposed projects. Final evaluations will be based on in-class work and interaction with the staff during the course as well as on a final live demo presentation of the projects. The focus of the course will not be on mathematical formalism and rigorous theorem proving, but rather on developing general intuition, creativity, practical implementation and model debugging skills.