Dieses Bild zeigtAndré Mielke

André Mielke

Herr Dr.-Ing.

Lehrbeauftragter
Institut für Statik und Dynamik der Luft- und Raumfahrtkonstruktionen

Kontakt

+49 711 685 53706
Visitenkarte: VCF | QR

Pfaffenwaldring 27
70569 Stuttgart
Germany
Raum: 00.052

Fachgebiet

  • Finite-Elemente-Analyse
  • Machine Learning
  • Quantum Computing

  1. 2026

    1. Mielke, A. (2026). Surrogate modeling for mechanical simulations: from classical data-driven approaches to quantum-enhanced physics-informed learning (Dissertation No. 5, Institut für Statik und Dynamik der Luft- und Raumfahrtkonstruktionen, Universität Stuttgart; p. xxi + 337). https://doi.org/10.18419/opus-18568

  2. 2023

    1. Mandl, L., Mielke, A., Seyedour, S. M., & Ricken, T. (2023). Affine transformations accelerate the training of physics-informed neural networks of a one-dimensional consolidation problem. Scientific Reports, 13, Article 15566. https://doi.org/10.1038/s41598-023-42141-x

  3. 2021

    1. Lambers, L., Mielke, A., & Ricken, T. (2021). Semi-automated Data-driven FE Mesh Generation and Inverse Parameter Identification for a Multiscale and Multiphase Model of Function-Perfusion Processes in the Liver. Pamm, 21, Article 1. https://doi.org/10.1002/pamm.202100190
    2. Egli, F. S., Straube, R. C., Mielke, A., & Ricken, T. (2021). Surrogate Modeling of a Nonlinear, Biphasic Model of Articular Cartilage with Artificial Neural Networks. Pamm, 21, Article 1. https://doi.org/10.1002/pamm.202100188
    3. Pi Savall, B., Mielke, A., & Ricken, T. (2021). Data-Driven Stress Prediction for Thermoplastic Materials. Pamm, 21, Article 1. https://doi.org/10.1002/pamm.202100225
    4. Mielke, A., & Ricken, T. (2021). Solving linear equation systems on noisy intermediate--scale quantum computers. Pamm, 20, Article 1. https://doi.org/10.1002/pamm.202000266
    5. Mielke, A., & Ricken, T. (2021). Finite element analysis of a 2D cantilever on a noisy intermediate-scale quantum computer. Pamm, 21, Article 1. https://doi.org/10.1002/pamm.202100246

  4. 2019

    1. Mielke, A., & Ricken, T. (2019). Evaluating Artificial Neural Networks and Quantum Computing for Solving Mechanical Boundary Value Problems. In A. Zingoni (ed.), Advances in Engineering Materials, Structures and Systems: Innovations, Mechanics and Applications (pp. 537–542). CRC Press-Balkema.
    2. Mielke, A., & Ricken, T. (2019). Evaluating Artificial Neural Networks and Quantum Computing for Mechanics. In PAMM (No. 1; Vol. 19, p. e201900470). https://doi.org/10.1002/pamm.201900470
  • Multiscale and Multiphase Materials (SS18)
  • Simulation of Coupled Problems (WS18/19)
  • Seminar Angewandte Finite Elemente (WS18/19) (WS19/20)
  • Nichtlineare Finite-Elemente-Methode (SS19)
  • Finite-Elemente-Methode II (WS19/20)
  • Methoden des maschinellen Lernens in der Mechanik (SS20, SS21, SS22, SS23,SS24,SS26)
  • Applied Machine Learning for Engineers (WS20/21, WS21/22, WS22/23, WS23/24,WS24/25)
  • Quantum Computing for Engineers (WS20/21, WS21/22, WS22/23, WS23/24,WS24/25,WS25/26)
  • Quantum Computing: Advanced Strategies (SS26)

Machine Learning

Finite-Elemente-Analyse

Quantum Computing

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