Jörg Nick

About Me

I am a mathematician specializing in numerical analysis, partial differential equations, and scientific machine learning. My research focuses on developing efficient numerical methods for wave phenomena and the mathematical foundations of metamaterials. I completed my doctorate at the University of Tübingen, where my work was part of the Collaborative Research Center (CRC) 1173, focusing on time-dependent boundary integral equations for wave scattering problems. After that, I worked as a Post-Doc at the Seminar of Applied Mathematics (SAM) at ETH Zürich, where I further explored effective numerical methods for wave propagation phenomena, as well as inverse scattering in the context of time-domain data. Recently, I have contributed towards the development and analysis of discretizations based on unstructured parametrizations, including neural networks, for time-dependent partial differential equations. Currently I am a postdoctoral researcher at the University of Geneva, continuing this work, working with domain decomposition techniques like the method of reflections as well as time parallel time stepping methods.

Research Interests

• Numerical analysis of partial differential equations
• Computational methods for wave phenomena
• Boundary integral equations
• Scientific machine learning applications in mathematics
• Mathematical foundations of metamaterials

Preprints

• T. Chaumont-Frelet, H. Gimperlein, I. Labarca-Figueroa & J. Nick (2025).
  A-posteriori error estimates and space-adaptive mesh refinements for time-dependent scattering problems..
  ArXiv Preprint ArXiv:2509.04217.
• J. Cao & J. Nick (2025).
  Exceptional point enhanced small particle detection in systems of subwavelength resonators.
  ETH Zürich, Seminar for Applied Mathematics, Research Reports.
• C. Lubich & J. Nick (2025).
  Regularized dynamical parametric approximation of stiff evolution problems.
  ETH Zürich, Seminar for Applied Mathematics, Research Reports.
• J. M. Melenk & J. Nick (2024).
  Parsimonious convolution quadrature.
  ETH Zürich, Seminar for Applied Mathematics, Research Reports.
• M. Feischl, C. Lasser, C. Lubich & J. Nick (2024).
  Regularized dynamical parametric approximation.
  ETH Zürich, Seminar for Applied Mathematics, Research Reports.

Peer-reviewed Publications

• J. Nick, R. Hiptmair & H. Ammari (2024).
  Wave scattering with time-periodic coefficients: Energy estimates and harmonic formulations.
  accepted to Mathematics of Computation.
• M. Knöller & J. Nick (2024).
  The temporal domain derivative in inverse acoustic obstacle scattering.
  accepted to Numerische Mathematik.
• J. Nick, S. Burkhard & C. Lubich (2024).
  Time-dependent electromagnetic scattering from dispersive materials.
  IMA Journal of Numerical Analysis, (electronically published) .
• J. Nick (2024).
  Numerical analysis for electromagnetic scattering with nonlinear boundary conditions.
  Mathematics of Computation, 93, 1529-1568.
• J. Nick, B. Kovács & C. Lubich (2022).
  Time-dependent electromagnetic scattering from thin layers.
  Numerische Mathematik, 150(4), 1123-1164.
• L. Banjai, C. Lubich & J. Nick (2022).
  Time-dependent acoustic scattering from generalized impedance boundary conditions via boundary elements and convolution quadrature.
  IMA Journal of Numerical Analysis, 42(1), 1-26.
• J. Nick, B. Kovács & C. Lubich (2021).
  Correction to: Stable and convergent fully discrete interior-exterior coupling of Maxwell's equations.
  Numerische Mathematik, 147(4), 997-1000.

Conference Papers

• J. Werner, C. Gerum, J. Nick, M. Le Floch, F. Brinkmann, J. Hampe & O. Bringmann (2025).
  Enhanced Anomaly Detection for Capsule Endoscopy Using Ensemble Learning Strategies.
  Accepted at the 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS EMBC)
• J. Werner, C. Gerum, M. Reiber, J. Nick & O. Bringmann (2023).
  Precise localization within the GI tract by combining classification of CNNs and time-series analysis of HMMs.
  In Machine Learning in Medical Imaging (pp. 174-183). Cham: Springer Nature Switzerland.
• J. Schmidt, P. Hennig, J. Nick & F. Tronarp (2023).
  The rank-reduced Kalman filter: Approximate dynamical-low-rank filtering in high dimensions.
  Advances in Neural Information Processing Systems, 36, 61364-61376.

Contact

Email: joerg.nick@math.ethz.ch

Office: HG G 56.1
Department of Mathematics
ETH Zürich
Rämistrasse 101
CH-8092 Zürich, Switzerland