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preprints
Extension Operators for Fractional Sobolev Spaces on Lipschitz Submanifolds
Preprint in arXiv, 2025
Analysis-Aware Defeaturing of Dirichlet Features
Preprint in arXiv, 2025
A Certified Goal-Oriented A Posteriori Defeaturing Error Estimator for Elliptic PDEs
Preprint in arXiv, 2025
publications
Optimal strokes for the 4-sphere swimmer at low Reynolds number in the regime of small deformations
Published in MathematicS In Action, 2022
Lumped Parameter Modeling of the Hypoplastic Left Heart Syndrome During Birth
Published in Functional Imaging and Modeling of the Heart, 2025
Nonlinear model reduction with Neural Galerkin schemes on quadratic manifolds
Published in Journal of Computational Physics, 2025
Galerkin neural network-POD for acoustic and electromagnetic wave propagation in parametric domains
Published in Advances in Computational Mathematics, 2025
talks
Galerkin Neural Network-POD for Acoustic and Electromagnetic Wave Propagation
Published:
Talk on the POD-NN method for wave propagation in parameterized domains based on this preprint. The abstract of the talk can be found here.
Analysis-Aware Defeaturing of Dirichlet Features in Poisson Problems
Published:
Talk on analysis-aware defeaturing of Dirichlet features in Poisson problems, including goal-oriented estimates. The abstract of the talk can be found here.
Defeaturing Error Estimates for Poisson Problems with Dirichlet Features
Published:
Geometry simplification, also known as defeaturing, is crucial for industrial simulations. It not only simplifies the meshing process but also reduces the computational costs of subsequent simulations by decreasing the number of degrees of freedom. Traditional defeaturing methods often rely on geometric criteria alone, overlooking the underlying physics of the problem. In contrast, analysis-aware defeaturing employs a posteriori error estimation, combining the defeatured simulation outputs with the exact geometry information to better inform the defeaturing process.
Analysis-Aware Defeaturing of Dirichlet Features in Poisson Problems
Published:
Talk on certified a posteriori defeaturing error estimation, including goal-oriented estimates and features subject to Dirichlet boundary conditions.
teaching
Numerical methods for physicists
Teaching assistant, ETHZ, D-MATH, 2019
Analysis I for civil engineers
Teaching assistant, ETHZ, D-MATH, 2020
Analysis III for computer science
Main teaching assistant, EPFL, Institute of Mathematics, 2024
Parallel and High-Performance Computing
Main teaching assistant, EPFL, Institute of Mathematics, 2025