Olga Mula

Olga Mula is a researcher at Eindhoven University of Technology (TU Eindhoven) in the Netherlands specializing in optimal transport theory, Wasserstein spaces, and model reduction techniques for partial differential equations. Her work bridges theoretical mathematics with practical applications in state estimation and inverse problems.

Her research interests focus on Optimal Transport, Wasserstein Spaces, Model Reduction, and Numerical Analysis of PDEs. She develops algorithms for state estimation in metric spaces, particularly focusing on the Wasserstein space of probability measures. Her work includes developing reduced models using barycentric approximation, analyzing convergence properties, and addressing challenges in sensor placement for optimal data acquisition.

Her recent publications demonstrate significant contributions to understanding how to build efficient reduced models in Wasserstein spaces for both forward and inverse problems. She has developed theoretical frameworks for state estimation algorithms, analyzed their performance in terms of Kolmogorov widths, and created practical implementations using sparse Wasserstein barycenters. Her work spans pure mathematical theory to applications in image processing and PDE-constrained optimization.

Her scientific contributions include:

• Development of piecewise-affine algorithms for state estimation
• Nonlinear model reduction on metric spaces for conservative PDEs
• Sparse approximation using Wasserstein barycenters
• Applications to shape reconstruction and line completion
• Theoretical analysis of approximation rates in Wasserstein spaces

She collaborates extensively with leading researchers in the field including Cohen, Dahmen, Feydy, and Rai. Her work demonstrates both theoretical depth and practical relevance, connecting abstract mathematical concepts to real-world problems in data assimilation and inverse modeling.