Research Topics
Mathematical models are ubiquitous in applications across science and technology, where these models are used to describe complex processes. To systematically investigate and predict phenomena of interest using computer simulations based on these models, we need accurate, reliable, and efficient computational methods.
Many complex models of practical relevance are affected by uncertainties, for example due to a lack of knowledge of material properties, intrinsic natural variability, or by incorporating noisy data. Such uncertainties inevitably impact the considered mathematical model and consequently the quantification of the reliability of simulation outcomes.
The junior research group Uncertainty Quantification (UQ) develops advanced mathematical and numerical techniques for the treatment and quantification of uncertainties at the interface of complex computational models and data. To this end, we work on methods of modern applied mathematics, machine learning techniques in conjunction with simulation-based approaches, and aspects of high-performance computing. Overall, our research focuses on theoretical and methodological aspects, as well as on interdisciplinary projects where theoretical sound methodologies are tailored to applications.
Publications
Hoang, T.-V.; Krumscheid, S.; Matthies, H. G.; Tempone, R.
2023. Foundations of Data Science, 5, 56–80. doi:10.3934/fods.2022016
Krumscheid, S.; Pettersson, P.; Yuan, F.
2023, February 21. doi:10.5281/zenodo.7660514
Ayoul-Guilmard, Q.; Ganesh, S.; Krumscheid, S.; Nobile, F.
2023. International Journal for Uncertainty Quantification, 13 (5), 61–98. doi:10.1615/Int.J.UncertaintyQuantification.2023045259
Boyko, V.; Krumscheid, S.; Vercauteren, N.
2022. Multiscale Modeling & Simulation, 20 (4), 1251–1283. doi:10.1137/21M1403989
Rodal, M.; Krumscheid, S.; Madan, G.; Henry LaCasce, J.; Vercauteren, N.
2022. Chaos: An Interdisciplinary Journal of Nonlinear Science, 32 (11), Art.-Nr.: 113139. doi:10.1063/5.0089694
Gasda, S.; Keilegavlen, E.; Sandve, T. H.; Berge, R.; Pettersson, P.; Krumscheid, S.
2022. Proceedings of the 16th Greenhouse Gas Control Technologies Conference. 16th International Conference on Greenhouse Gas Control Technologies (GHGT-16) 23-27 Oct 2022. doi:10.2139/ssrn.4277020
Lau, F. D.-H.; Krumscheid, S.
2022. METRON, 80, Art.Nr. 106003. doi:10.1007/s40300-022-00235-y
Pettersson, P.; Krumscheid, S.
2022. International Journal for Uncertainty Quantification, 12 (6), 71–99. doi:10.1615/Int.J.UncertaintyQuantification.2022041034
Martin, M.; Krumscheid, S.; Nobile, F.
2021. ESAIM: Mathematical Modelling and Numerical Analysis, 55 (4), 1599–1633. doi:10.1051/m2an/2021025
Zinner, C.; Matzka, M.; Krumscheid, S.; Holmberg, H.-C.; Sperlich, B.
2021. Journal of Human Kinetics, 77 (1), 117–123. doi:10.2478/hukin-2021-0016
Kaiser, A.; Faranda, D.; Krumscheid, S.; Belušić, D.; Vercauteren, N.
2020. Journal of the Atmospheric Sciences, 77 (8), 2921–2940. doi:10.1175/JAS-D-19-0287.1
Krumscheid, S.; Nobile, F.; Pisaroni, M.
2020. Journal of Computational Physics, 414, Article no: 109466. doi:10.1016/j.jcp.2020.109466
Hoel, H.; Krumscheid, S.
2019. Journal of Complexity, 54, Art.-Nr.: 101407. doi:10.1016/j.jco.2019.05.001
Rey, V.; Krumscheid, S.; Nobile, F.
2019. International Journal of Engineering Science, 138, 50–64. doi:10.1016/j.ijengsci.2019.02.003
Krumscheid, S.
2018. Mathematical Models and Methods in Applied Sciences, 28 (08), 1565–1597. doi:10.1142/S0218202518500434
Krumscheid, S.; Nobile, F.
2018. SIAM/ASA Journal on Uncertainty Quantification, 6 (3), 1256–1293. doi:10.1137/17M1135566
Krumscheid, S.; Pradas, M.; Pavliotis, G. A.; Kalliadasis, S.
2015. Physical Review E, 92 (4), Article no: 042139. doi:10.1103/PhysRevE.92.042139
Kalliadasis, S.; Krumscheid, S.; Pavliotis, G. A.
2015. Journal of Computational Physics, 296, 314–328. doi:10.1016/j.jcp.2015.05.002
Haas, S.; Krumscheid, S.; Bauer, A.; Lambertz, A.; Rau, U.
2013. Progress in Photovoltaics: Research and Applications, 21 (5), 972–979. doi:10.1002/pip.2188
Krumscheid, S.; Pavliotis, G. A.; Kalliadasis, S.
2013. Multiscale Modeling & Simulation, 11 (2), 442–473. doi:10.1137/110854485