QUEENS - A general purpose framework for Uncertainty Quantification, Physics-Informed Machine Learning, Bayesian Optimization, Inverse Problems and Simulation Analytics on distributed computer systems.
Publications in peer-reviewed international journals
Jonas Biehler, Jonas Nitzler, Sebastian Brandstaeter, Maximilian Dinkel, Volker Gravemeier, Lea J. Haeusel, Gil Robalo Rei, Harald Willmann, Barbara Wirthl, Wolfgang A. Wall (2026), QUEENS: an open-source Python framework for solver-independent analyses of large-scale computational models – from parameter studies and identification, sensitivity analysis, surrogates, optimization, (Bayesian) forward and backward uncertainty quantification to digital twinning, Engineering with Computers, 42, 147. DOI:10.1007/s00366-026-02337-x
Lea J. Haeusel, Jonas Nitzler, Lea J. Köglmeier, Wolfgang A. Wall (2026), Multi-physics-enhanced Bayesian inverse analysis: Information gain from additional fields, Computer Methods in Applied Mechanics and Engineering, 452, 118735. DOI:10.1016/j.cma.2026.118735
Jonas F. Eichinger, Lea J. Haeusel, Daniel Paukner, Roland C. Aydin, Jay D. Humphrey, Christian J. Cyron (2021), Mechanical homeostasis in tissue equivalents: a review, Biomechanics and Modeling in Mechanobiology, 20, 833–850. DOI:10.1007/s10237-021-01433-9
Education
since 2024 Research Associate at the Institute for Computational Mechanics (Lehrstuhl für Numerische Mechanik), Technische Universität München, Germany
2024 Master of Science (M.Sc.), Mechanical Engineering, Technische Universität München, Germany
2024 Master of Science (M.Sc.), Mechatronics and Robotics, Technische Universität München, Germany
2020 Bachelor of Science (B.Sc.), Engineering Science, Technische Universität München, Germany
