Paul Schwerdtner
Postdoctoral Researcher
Courant Institute of Mathematical Sciences, New York University
Interests
- High performance computing
- Model order reduction for large-scale simulations
- Numerical linear algebra
- Robust controller design and Scientific Machine Learning
Experience & Education
| Courant Institute, NYU | Postdoctoral Researcher | Jun 2023 - present |
| Tech. Univ. Berlin | Scientific Assistant | Oct 2018 - Mar 2023 |
| Mitsubishi Electric Research Labs | Research Intern | Oct 2018 - Mar 2023 |
| Continental AG | R&D Intern | Nov 2015 - Mar 2016 |
| 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 |
Recent Projects
Quadratic Manifolds
Quadratic Manifolds
Utilizing nonlinear dependencies in latent space
Quadratic manifolds augment linear approximations with quadratic correction terms. This is useful for finding efficient representations of high-dimensional data points to capture fine details with low-dimensional surrogates.
Contributions
- Greedy algorithm leading to orders of magnitudes improvement in accuracy
- Streaming-based manifold computation to process petabyte-scale simulation data
- Applications to complex flow problems in collaboration with National Labs (LANL & NREL)
Related 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
- Video: Talk at MORE-Conference (San Diego, 2024)
Multilevel Monte Carlo wildfire simulation
Multilevel Monte Carlo wildfire simulation
Enabling uncertainty quantification at operational scales
Developed methodology for merging multiple data sources for estimating burned areas in large-scale coupled atmospheric wildfire simulations.
Contributions
- Contributed uncertainty quantification method to Google-research wildfire simulation framework swirl_lm
- Multilevel Monte Carlo simulation to enable uncertainty quantification at operational scales
- Designed large-scale simulation study using Google-Cloud TPUs to simulate wildfire scenarios in California