For more information please contact Prof. dr. ir. Karl Meerbergen (firstname.lastname@example.org; phone:+32 16 327959) or dr. ir. Ward Melis (email@example.com; phone:+32 16 320616).
You can apply for this job no later than October 15, 2020 via the online application tool
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At the Department of Computer Science of KU Leuven, the research unit NUMA works on numerical methods, algorithms and software for simulation and data analysis, with applications in many fields in science and engineering. The research in NUMA on materials engineering focuses on multi-scale simulation, high performance computing and model order reduction. NUMA is partner in a 3-year research project on Process Simulation in Additive Manufacturing, funded by Strategic Initiative on Materials (SIM-Flanders, https://www.sim-flanders.be). Other partners are Siemens, Materialise, SABCA, ESMA and two other departments at KU Leuven, Materials Engineering (MTM) and Mechanical Engineering.
Additive Manufacturing (AM) is becoming important in industry since it allows for virtually unlimited design possibilities with minimal additional costs. However, insights in and control of the AM print process are still limited and often based on trial-and-error. The project, in which this vacancy fits, aims at understanding how phenomena at different length and time scales interact during the metal AM printing and post- processing steps to define the final performance of a part. Multi-physics,multi-scale process simulation tools will be developed and validated with measurements. To ensure efficient execution of the simulation workflow novel efficient numerical tools must be developed.The aim of this postdoc position is to reduce the computational complexity and cost of local microstructural simulations by constructing surrogate models. These microstructural grain growth simulations are developed and performed by the Department of Materials Science (MTM) using phase-field models. In general, surrogate or reduced models represent the input/output map of a complex system by a compact, cheap-to-evaluate model, and this for a wide range of inputs without the need to simulate a computationally expensive high-fidelity model. Since, in this project, the inputs and outputs will generally be high-dimensional, the possibilities of high-dimensional tensor decompositions will be explored. The focus will lie on surrogate or reduced models by using the tensor cross approximation to determine a low-rank tensor representation of the input/output map. In collaboration with MTM, the necessary input and output parameters and their range to be considered for the surrogate models will be identified.