Surrogate models for crystal plasticity - predicting stress, strain and dislocation density over time: Unterschied zwischen den Versionen
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|vortragstyp=Proposal | |vortragstyp=Proposal | ||
|betreuer=Daniel Betsche | |betreuer=Daniel Betsche | ||
|termin=Institutsseminar/2022- | |termin=Institutsseminar/2022-09-02 | ||
|vortragsmodus=in Präsenz | |vortragsmodus=in Präsenz | ||
|kurzfassung=When engineers design structures, prior knowledge of how they react to external forces is crucial. Applied forces introduce stress, leading to dislocations of individual molecules that ultimately may cause material failure, like cracks, if the internal strain of the material exceeds a certain threshold. We can observe this by applying increasing physical forces to a structure and measure the stress, strain and the dislocation density curves. | |kurzfassung=When engineers design structures, prior knowledge of how they will react to external forces is crucial. Applied forces introduce stress, leading to dislocations of individual molecules that ultimately may cause material failure, like cracks, if the internal strain of the material exceeds a certain threshold. We can observe this by applying increasing physical forces to a structure and measure the stress, strain and the dislocation density curves. | ||
Finite Elemente Analysis (FEM) enables the simulation of a material deforming under external forces, but it comes with very high computational costs. This makes it unfeasible to conduct a large number of simulations with varying parameters. | Finite Elemente Analysis (FEM) enables the simulation of a material deforming under external forces, but it comes with very high computational costs. This makes it unfeasible to conduct a large number of simulations with varying parameters. | ||
In this thesis, we use neural network based sequence models to build a data-driven surrogate model that predicts stress, strain and dislocation density curves produced by an FEM-simulation based on the simulation’s input parameters. | In this thesis, we use neural network based sequence models to build a data-driven surrogate model that predicts stress, strain and dislocation density curves produced by an FEM-simulation based on the simulation’s input parameters. | ||
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Aktuelle Version vom 25. August 2022, 09:48 Uhr
| Vortragende(r) | Benjamin Jochum | |
|---|---|---|
| Vortragstyp | Proposal | |
| Betreuer(in) | Daniel Betsche | |
| Termin | Fr 2. September 2022 | |
| Vortragssprache | ||
| Vortragsmodus | in Präsenz | |
| Kurzfassung | When engineers design structures, prior knowledge of how they will react to external forces is crucial. Applied forces introduce stress, leading to dislocations of individual molecules that ultimately may cause material failure, like cracks, if the internal strain of the material exceeds a certain threshold. We can observe this by applying increasing physical forces to a structure and measure the stress, strain and the dislocation density curves.
Finite Elemente Analysis (FEM) enables the simulation of a material deforming under external forces, but it comes with very high computational costs. This makes it unfeasible to conduct a large number of simulations with varying parameters. In this thesis, we use neural network based sequence models to build a data-driven surrogate model that predicts stress, strain and dislocation density curves produced by an FEM-simulation based on the simulation’s input parameters. | |