Paul Schwerdtner
Scholar

Paul Schwerdtner

Google Scholar ID: OKQsA9UAAAAJ
Courant Institute of Mathematical Sciences, NYU
Robust ControlModel Order ReductionNumerical Mathematics
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Citations & Impact
All-time
Citations
280
 
H-index
12
 
i10-index
13
 
Publications
20
 
Co-authors
0
 
Contact
Emailpaul.schwerdtner@nyu.eduCVOpen ↗
Publications
2 items
Hankel Singular Value Regularization for Highly Compressible State Space Models
2025
Cited
0
Operator Inference Aware Quadratic Manifolds with Isotropic Reduced Coordinates for Nonintrusive Model Reduction
2025
Cited
0
Resume (English only)
Academic Achievements
  • Publications:
  • - P. Schwerdtner, B. Peherstorfer, Greedy construction of quadratic manifolds for nonlinear dimensionality reduction and nonlinear model reduction, 2024
  • - P. Schwerdtner, P. Mohan, A. Pachalieva, J. Bessac, D. O'Malley, B. Peherstorfer, Online learning of quadratic manifolds from streaming data for nonlinear dimensionality reduction and nonlinear model reduction, Proceedings of the Royal Society A, 2025
  • - P. Schwerdtner, F. Law, Q. Wang, C. Gazen, Y.-F. Chen, M. Ihme, and B. Peherstorfer, Uncertainty quantification in coupled wildfire–atmosphere simulations at scale, PNAS Nexus, 2024
  • Projects:
  • - Quadratic Manifolds: Utilizing nonlinear dependencies in latent space to augment linear approximations with quadratic correction terms, leading to orders of magnitudes improvement in accuracy, and applications to complex flow problems.
  • - Multilevel Monte Carlo wildfire simulation: Developed methodology for merging multiple data sources for estimating burned areas in large-scale coupled atmospheric wildfire simulations, enabling uncertainty quantification at operational scales using Google-Cloud TPUs.
Research Experience
  • Postdoctoral Researcher, Courant Institute, NYU, Jun 2023 - present; Scientific Assistant, Tech. Univ. Berlin, Oct 2018 - Mar 2023; Research Intern, Mitsubishi Electric Research Labs, Oct 2018 - Mar 2023; R&D Intern, Continental AG, Nov 2015 - Mar 2016.
Education
  • Ph.D. in Mathematics, TU Berlin, Germany, Oct 2018 - Jan 2023; M.S. in Engineering Science, TU Berlin, Germany, Oct 2016 - Apr 2018; B.S. in Engineering Science, TU Berlin, Germany, Oct 2012 - Apr 2016.
Background
  • Interests: High performance computing, Model order reduction for large-scale simulations, Numerical linear algebra, Robust controller design and Scientific Machine Learning.
Co-authors
0 total
Co-authors: 0 (list not available)