SIAM Early Career Prize for Nora Lüthen
A great honour for Nora Lüthen, a?postdoctoral researcher?in the Department of Civil, Environmental and Geomatic Engineering at ETH Zurich: She has received an Early Career Prize from the US Society for Industrial and Applied Mathematics for her mathematical research on the acceleration of computer simulations.
The Society for Industrial and Applied Mathematics (SIAM) pursues the goal of advancing the application of mathematics and thus enabling new technical methods. To this end, the organisation supports young researchers with grants and prizes.
One of these prestigious awards has now gone to Nora Lüthen from the Chair of Risk, Safety and Uncertainty Quantification, which is headed by Prof. Bruno Sudret. She has received the SIAM Activity Group on Uncertainty Quantification Early Career Prize, which is awarded every two years to one individual in their early career for outstanding research contributions in the field of uncertainty quantification. In its tribute, the jury emphasised that Lüthen “has tackled the challenging task of developing surrogate models for stochastic simulators.”
How to accelerate computing processes
Many technical systems such as energy grids, buildings, transport systems and medical devices are now designed and operated with the help of complex computer simulations. These models often require a great deal of computing time. If a single simulation takes several days, only a few scenarios can be tested, leaving many potential risks unrecognised.
This is where Lüthen’s research in the field of surrogate modelling comes in. She develops mathematical models that learn from a small number of simulations and use them to create fast but accurate approximations, known as surrogate models. “Specifically, our Chair is working with stochastic simulators – that is, models with random influences. Using surrogate models, scientists and engineers can investigate thousands of ‘what if’ scenarios in seconds instead of months,” Lüthen says.
Broad potential for application
Such models can be used, for example, in the construction of wind turbines or in earthquake engineering, where the inputs are determined by turbulent wind fields or unpredictable movements of the subsoil. Applications are also opening up in epidemiology and financial mathematics.
Lüthen’s research has led, among other things, to a widely cited overview study as well as several extensions and a new benchmarking module for UQLab – the uncertainty quantification framework developed at ETH Zurich.
Nora Lüthen
After completing her Master’s degree in mathematics at the University of Bonn, Nora Lüthen received her doctorate from ETH Zurich in 2022 with a doctoral thesis on spectral surrogate models with sparse representation for deterministic and stochastic computer simulations. In addition to her research at the Chair of Risk, Safety and Uncertainty Quantification, she is also heavily involved in teaching. She teaches engineering students in programming, scientific computing and uncertainty quantification. She is particularly keen to convey the mathematical and computational principles that are necessary for reliable and uncertainty-aware simulation models.