Michael Hecht

According to our database, Michael Hecht authored at least 19 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Ensuring Topological Data-Structure Preservation under Autoencoder Compression Due to Latent Space Regularization in Gauss-Legendre Nodes.

Chethan Krishnamurthy Ramanaik, Anna Willmann, Juan Esteban Suarez Cardona, Pia Hanfeld, Nico Hoffmann, Michael Hecht

Axioms, August, 2024

A note on the rate of convergence of integration schemes for closed surfaces.

Gentian Zavalani, Elima Shehu, Michael Hecht

Comput. Appl. Math., March, 2024

Hybrid Surrogate Models: Circumventing Gibbs Phenomenon for Partial Differential Equations with Finite Shock-Type Discontinuities.

Juan Esteban Suarez Cardona, Shashank Reddy, Michael Hecht

CoRR, 2024

High-order numerical integration on regular embedded surfaces.

Gentian Zavalani, Michael Hecht

CoRR, 2024

PMBO: Enhancing Black-Box Optimization through Multivariate Polynomial Surrogates.

Janina Schreiber, Pau Batlle, Damar Wicaksono, Michael Hecht

CoRR, 2024

2023

Polynomial differentiation decreases the training time complexity of physics-informed neural networks and strengthens their approximation power.

Juan Esteban Suarez Cardona, Michael Hecht

Mach. Learn. Sci. Technol., December, 2023

UQTestFuns: A Python3 library of uncertainty quantification (UQ) test functions.

Damar Wicaksono, Michael Hecht

J. Open Source Softw., November, 2023

Global Polynomial Level Sets for Numerical Differential Geometry of Smooth Closed Surfaces.

Sachin K. Thekke Veettil, Gentian Zavalani, Uwe Hernandez Acosta, Ivo F. Sbalzarini, Michael Hecht

SIAM J. Sci. Comput., August, 2023

High-Order Integration on regular triangulated manifolds reaches Super-Algebraic Approximation Rates through Cubical Re-parameterizations.

Gentian Zavalani, Oliver Sander, Michael Hecht

CoRR, 2023

Polynomial-Model-Based Optimization for Blackbox Objectives.

Janina Schreiber, Damar Wicaksono, Michael Hecht

CoRR, 2023

Learning Partial Differential Equations by Spectral Approximates of General Sobolev Spaces.

Juan Esteban Suarez Cardona, Phil-Alexander Hofmann, Michael Hecht

CoRR, 2023

Minimizing Black Boxes due to Polynomial-Model-Based Optimization.

Janina Schreiber, Damar Wicaksono, Michael Hecht

Proceedings of the Companion Proceedings of the Conference on Genetic and Evolutionary Computation, 2023

2022

Multivariate Polynomial Regression of Euclidean Degree Extends the Stability for Fast Approximations of Trefethen Functions.

Sachin K. Thekke Veettil, Yuxi Zheng, Uwe Hernandez Acosta, Damar Wicaksono, Michael Hecht

CoRR, 2022

Replacing Automatic Differentiation by Sobolev Cubatures fastens Physics Informed Neural Nets and strengthens their Approximation Power.

Juan Esteban Suarez Cardona, Michael Hecht

CoRR, 2022

2021

Tight Localizations of Feedback Sets.

Michael Hecht, Krzysztof Gonciarz, Szabolcs Horvát

ACM J. Exp. Algorithmics, 2021

InFlow: Robust outlier detection utilizing Normalizing Flows.

Nishant Kumar, Pia Hanfeld, Michael Hecht, Michael Bussmann, Stefan Gumhold, Nico Hoffmann

CoRR, 2021

2020

Multivariate Interpolation on Unisolvent Nodes - Lifting the Curse of Dimensionality.

Michael Hecht, Krzysztof Gonciarz, Jannik Michelfeit, Vladimir Sivkin, Ivo F. Sbalzarini

CoRR, 2020

2018

Exact Localisations of Feedback Sets.

Michael Hecht

Theory Comput. Syst., 2018

2017

A generalization of the most common subgraph distance and its application to graph editing.

Michael Hecht

Pattern Recognit. Lett., 2017