The Special Interest Group Uncertainty Quantification and Scientific Machine Learning brings together researchers working at the intersection of uncertainty quantification and scientific machine learning with applications in computational science and engineering. The particular focus is on methods that rigorously account for uncertainty in data-driven and model-based problems. The topics of interest include, but are not limited to, uncertainty quantification which deals with probabilistic methods, Bayesian approaches, inverse problems, stochastic modeling, as well as scientific machine learning in the sense of physics-informed neural networks, integrating physical laws and differential equations with data-driven machine learning.

Bojana Rosic

Bojana Rosić is from Serbia and studied mechanical engineering with a focus on applied mechanics and automatic control at the University of Kragujevac (Serbia). As her research interests shifted toward the combination of computer science and mechanical engineering during her doctoral studies, she completed a dual doctoral program in Applied Mathematics at the Technical University of Braunschweig and the University of Kragujevac, which she completed in 2012 summa cum laude—as she had with all her previous degrees. Her dissertation is titled: “Variational formulations and functional approximation algorithms in stochastic plasticity of materials, opens an external URL in a new window” and was recognized by the German Association for Computer-Aided Mechanics (GACM) as the best doctoral dissertation in Germany. Based on the results of her doctoral work, she was also named a GAMM Junior Fellow, opens an external URL in a new window (for three years) by the German Association for Applied Mathematics and Mechanics (GAMM). After approximately seven years as a postdoc at the Institute for Scientific Computing at the Technical University of Braunschweig, she began her position in May 2019 as a full professor of Applied Mechanics and Data Analysis at the University of Twente (Netherlands), where she focused specifically on topics such as computational sciences in engineering.

Leila Taghizadeh

Leila Taghizadeh is leading the Research Group on Uncertainty Quantification and am an FWF Elise Richter Fellow at the Institute for Analysis and Scientific Computing (ASC) at TU Wien. She is currently leading the FWF Elise Richter Project V1000. She is also a member of the Vienna Center for PDEs and the Society for Industrial and Applied Mathematics (SIAM). Her research focuses on the development of mathematical and computational methods for uncertainty quantification (UQ) and statistical inverse problems for complex systems in the fields of computational science and engineering. To this end, she draws on theories and methods from mathematics and statistics, including numerical methods for partial differential equations, probability theory and statistics, inverse problems, and optimization.

Activities

Forthcoming