| Linear solid mechanics |
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| Based on Bachelor-level engineering mechanics (“What happens if forces act on a body?”) we extend our knowledge about deformation and mechanical response to more complex materials, numerical solution schemes and their implementation. |
| Nonlinear solid mechanics |
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| Linear elasticity as considered in bachelor level mechanics is only realistic for small deformations and a limited range of materials. This course extends this concept by considering large elastic deformations, inelastic behavior (plasticity, damage) and time-dependent behavior (visco-elasticity). |
| Nonlinear finite element method |
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| The Finite Element Method is the most prominent method for solving boundary value problems. In many cases the underlying equations are non-linear. This course extends the knowledge from any introduction to FEM to mechanical systems with material or geometric non-linearity, as found in large-formation elasticity, plasticity or buckling. |
| Multi-scale methods |
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| Many materials are structured on a small scale and this structure, apart from the material properties of the constituents, influences the macroscopic behavior. Methods are taught which allow to transfer small scale properties to a larger scale, allowing for a better understanding why the material behaves like it does. |
| Mechanical testing of materials |
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| In the design phase, material models and their parameters are identified from real-world data collected in experiments. This practical course provides an overview of material testing in solid mechanics, including an introduction to common material models. The focus lays on the laboratory: you will conduct tests and evaluate the results. |
| Algorithms and Programming |
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| Concepts are taught that enable students to understand existing and especially to draft own R&D software. The course covers an introduction to Python, Object Oriented Programming, Data structures as well as selected algorithms. |
| Data-driven material modeling |
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| Material modelling comprises two tasks: specifying the functions which appear in the model, and finding the numerical values of their parameters. Data-driven material modelling aims to automate these tasks using machine learning and statistical methods. In particular, we will review the concepts of material modelling and and discuss model discovery through the lens of regression. |
| Advanced data-driven material modeling |
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| Inverse problems arise in the context of parameter identification from full-field or monitoring data. Especially monitoring imposes severe time constraints for their solution. This course discusses data-driven methods such as physics-informed neural networks to enable fast inference. |