Michael Hecht
Orcid: 0000-0001-9214-8253
According to our database1,
Michael Hecht
authored at least 30 papers
between 2017 and 2025.
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Collaborative distances:
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Book In proceedings Article PhD thesis Dataset OtherLinks
Online presence:
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Bibliography
2025
Dataset, January, 2025
Dataset, January, 2025
2024
Dataset, November, 2024
Ensuring Topological Data-Structure Preservation under Autoencoder Compression Due to Latent Space Regularization in Gauss-Legendre Nodes.
Axioms, August, 2024
surfpy is a Python package for computing surface integrals over smooth embedded manifolds.
Dataset, June, 2024
Comput. Appl. Math., March, 2024
Negative order sobolev cubatures: preconditioners of partial differential equation learning tasks circumventing numerical stiffness.
Mach. Learn. Sci. Technol., 2024
Hybrid Surrogate Models: Circumventing Gibbs Phenomenon for Partial Differential Equations with Finite Shock-Type Discontinuities.
CoRR, 2024
CoRR, 2024
2023
Polynomial differentiation decreases the training time complexity of physics-informed neural networks and strengthens their approximation power.
Mach. Learn. Sci. Technol., December, 2023
J. Open Source Softw., November, 2023
Dataset, October, 2023
Global Polynomial Level Sets for Numerical Differential Geometry of Smooth Closed Surfaces.
SIAM J. Sci. Comput., August, 2023
High-Order Integration on regular triangulated manifolds reaches Super-Algebraic Approximation Rates through Cubical Re-parameterizations.
CoRR, 2023
Learning Partial Differential Equations by Spectral Approximates of General Sobolev Spaces.
CoRR, 2023
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.
CoRR, 2022
Replacing Automatic Differentiation by Sobolev Cubatures fastens Physics Informed Neural Nets and strengthens their Approximation Power.
CoRR, 2022
2021
2020
Multivariate Interpolation on Unisolvent Nodes - Lifting the Curse of Dimensionality.
CoRR, 2020
2018
2017
A generalization of the most common subgraph distance and its application to graph editing.
Pattern Recognit. Lett., 2017