Publications

My complete list of publications, sorted by year of publication. A machine-readable list is available on my ORCID profile.

Journal articles

P. Trunschke, M. Eigel and A. Nouy.
Weighted sparsity and sparse tensor networks for least squares approximation.
The SMAI Journal of Computational Mathematics, 11:289–333, 2025. (arXiv)

P. Trunschke.
Sample complexity bounds for the local convergence of least squares approximation.
Analysis and Applications, 23(1):139–167, 2024. (arXiv)

M. Eigel, N. Farchmin, S. Heidenreich and P. Trunschke.
Adaptive nonintrusive reconstruction of solutions to high-dimensional parametric PDEs.
SIAM Journal on Scientific Computing, 45(2):A457–A479, 2023. (arXiv)

C. Bayer, M. Eigel, L. Sallandt and P. Trunschke.
Pricing high-dimensional Bermudan options with hierarchical tensor formats.
SIAM Journal on Financial Mathematics, 14(2):383–406, 2023. (arXiv)

M. Eigel, N. Farchmin, S. Heidenreich and P. Trunschke.
Efficient approximation of high-dimensional exponentials by tensor networks.
International Journal for Uncertainty Quantification, 13(1):25–51, 2023. (arXiv)

M. Eigel, R. Schneider and P. Trunschke.
Convergence bounds for empirical nonlinear least-squares.
ESAIM: Mathematical Modelling and Numerical Analysis, 56(1):79–104, 2022. (arXiv)

M. Götte, R. Schneider and P. Trunschke.
A block-sparse tensor train format for sample-efficient high-dimensional polynomial regression.
Frontiers in Applied Mathematics and Statistics, 7:702486, 2021. (arXiv)

A. Trunschke, G. Bellini, M. Boniface, et al. (including P. Trunschke).
Towards experimental handbooks in catalysis.
Topics in Catalysis, 63(19–20):1683–1699, 2020.

M. Eigel, R. Schneider, P. Trunschke and S. Wolf.
Variational Monte Carlo — bridging concepts of machine learning and high-dimensional partial differential equations.
Advances in Computational Mathematics, 45(5–6):2503–2532, 2019. (arXiv)

T. Streubel, C. Strohm, P. Trunschke and C. Tischendorf.
Generic construction and efficient evaluation of flow network DAEs and their derivatives in the context of gas networks.
In Operations Research Proceedings 2017, pages 627–632. Springer, 2018.

Preprints

M. Eigel, P. Trunschke and D. Wrischnig.
Multilevel sparse tensor approximation for high-dimensional parametric PDEs.
arXiv:2603.15284, 2026.

M. Casfor, P. Trunschke, N. Hegemann and S. Heidenreich.
Estimating systematic errors in Bayesian inversion using transport maps.
arXiv:2509.16116, 2025.

P. Trunschke and A. Nouy.
Optimal sampling for least squares approximation with general dictionaries.
arXiv:2407.07814, 2024.

N. Hegemann, A. Nouy and P. Trunschke.
Sample-based almost-sure quasi-optimal approximation in reproducing kernel Hilbert spaces.
arXiv:2407.06674, 2024.

R. Gruhlke, A. Nouy and P. Trunschke.
Optimal sampling for stochastic and natural gradient descent.
arXiv:2402.03113, 2024.

Doctoral thesis

P. Trunschke.
On the theory and practice of tensor recovery for high-dimensional partial differential equations.
Ph.D. thesis, Technische Universität Berlin, 2021.

Last updated: September 2026