A generalization of the most common subgraph distance and its application to graph editing. - Michael Hecht (Pattern Recognit. Lett., 2017)

Exact Localisations of Feedback Sets. - Michael Hecht (Theory Comput. Syst., 2018)

Multivariate Interpolation on Unisolvent Nodes - Lifting the Curse of Dimensionality. - Michael Hecht; Krzysztof Gonciarz; Jannik Michelfeit; Vladimir Sivkin; Ivo F. Sbalzarini (CoRR, 2020)

InFlow: Robust outlier detection utilizing Normalizing Flows. - Nishant Kumar 005; Pia Hanfeld; Michael Hecht; Michael Bussmann; Stefan Gumhold; Nico Hoffmann (CoRR, 2021)

Tight Localizations of Feedback Sets. - Michael Hecht; Krzysztof Gonciarz; Szabolcs Horvát (ACM J. Exp. Algorithmics, 2021)

Minterpy - multivariate polynomial interpolation. - Uwe Hernandez Acosta; Sachin Krishnan Thekke Veettil; Damar Canggih Wicaksono; Jannik Michelfeit; Michael Hecht (RODARE, 2021)

Minterpy - multivariate polynomial interpolation. - Uwe Hernandez Acosta; Sachin Krishnan Thekke Veettil; Damar Canggih Wicaksono; Jannik Michelfeit; Michael Hecht (RODARE, 2021)

Replacing Automatic Differentiation by Sobolev Cubatures fastens Physics Informed Neural Nets and strengthens their Approximation Power. - Juan Esteban Suarez Cardona; Michael Hecht (CoRR, 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)

Minimizing Black Boxes due to Polynomial-Model-Based Optimization. - Janina Schreiber; Damar Wicaksono; Michael Hecht (GECCO Companion, 2023)

Learning Partial Differential Equations by Spectral Approximates of General Sobolev Spaces. - Juan Esteban Suarez Cardona; Phil-Alexander Hofmann; Michael Hecht (CoRR, 2023)

Polynomial-Model-Based Optimization for Blackbox Objectives. - Janina Schreiber; Damar Wicaksono; Michael Hecht (CoRR, 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)

Minterpy - multivariate polynomial interpolation. - Uwe Hernandez Acosta; Sachin Krishnan Thekke Veettil; Damar Canggih Wicaksono; Jannik Michelfeit; Michael Hecht (RODARE, 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., 2023)

UQTestFuns: A Python3 Library of Uncertainty Quantification (UQ) Test Functions. - Damar Canggih Wicaksono; Michael Hecht (RODARE, 2023)

UQTestFuns: A Python3 library of uncertainty quantification (UQ) test functions. - Damar Wicaksono; Michael Hecht (J. Open Source Softw., 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., 2023)

PMBO: Enhancing Black-Box Optimization through Multivariate Polynomial Surrogates. - Janina Schreiber; Pau Batlle; Damar Wicaksono; Michael Hecht (CoRR, 2024)

High-order numerical integration on regular embedded surfaces. - Gentian Zavalani; Michael Hecht (CoRR, 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)

Negative order sobolev cubatures: preconditioners of partial differential equation learning tasks circumventing numerical stiffness. - Juan Esteban Suarez Cardona; Phil-Alexander Hofmann; Michael Hecht (Mach. Learn. Sci. Technol., 2024)

A note on the rate of convergence of integration schemes for closed surfaces. - Gentian Zavalani; Elima Shehu; Michael Hecht (Comput. Appl. Math., 2024)

surfpy is a Python package for computing surface integrals over smooth embedded manifolds. - Gentian Zavalani; Michael Hecht (RODARE, 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, 2024)

UQTestFuns: A Python3 Library of Uncertainty Quantification (UQ) Test Functions. - Damar Canggih Wicaksono; Michael Hecht (RODARE, 2024)

Minterpy - multivariate polynomial interpolation. - Uwe Hernandez Acosta; Sachin Krishnan Thekke Veettil; Damar Canggih Wicaksono; Jannik Michelfeit; Michael Hecht (RODARE, 2024)

Minterpy - multivariate polynomial interpolation. - Uwe Hernandez Acosta; Damar Canggih Wicaksono; Sachin Krishnan Thekke Veettil; Jannik Michelfeit; Michael Hecht (RODARE, 2024)

Data to "Minterpy: Multivariate polynomial interpolation in Python". - Damar Canggih Wicaksono; Uwe Hernandez Acosta; Sachin Krishnan Thekke Veettil; Jannik Kissinger; Michael Hecht (RODARE, 2025)

UQTestFuns: A Python3 Library of Uncertainty Quantification (UQ) Test Functions. - Damar Canggih Wicaksono; Michael Hecht (RODARE, 2025)

Multivariate Newton Interpolation in Downward Closed Spaces Reaches the Optimal Geometric Approximation Rates for Bos-Levenberg-Trefethen Functions. - Michael Hecht; Phil-Alexander Hofmann; Damar Wicaksono; Uwe Hernandez Acosta; Krzysztof Gonciarz; Jannik Kissinger; Vladimir Sivkin; Ivo F. Sbalzarini (CoRR, 2025)

Fast Newton Transform: Interpolation in Downward Closed Polynomial Spaces. - Phil-Alexander Hofmann; Damar Wicaksono; Michael Hecht (CoRR, 2025)

Minterpy: multivariate polynomial interpolation in Python. - Damar Wicaksono; Uwe Hernandez Acosta; Sachin Krishnan Thekke Veettil; Jannik Kissinger; Michael Hecht (J. Open Source Softw., 2025)

Interpolation in Polynomial Spaces of p-Degree. - Phil-Alexander Hofmann; Damar Wicaksono; Michael Hecht (CoRR, 2025)