Juan Esteban Suarez Cardona

According to our database, Juan Esteban Suarez Cardona authored at least 8 papers between 2022 and 2026.

Collaborative distances:

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

2026

A Continuous and Interpretable Morphometric for Robust Quantification of Dynamic Biological Shapes.

Roua Rouatbi, Juan Esteban Suarez Cardona, Alba Villaronga-Luque, Jesse V. Veenvliet, Ivo F. Sbalzarini

Proceedings of the 23rd IEEE International Symposium on Biomedical Imaging, 2026

2025

A Variational Framework for the Algorithmic Complexity of PDE Solutions.

Juan Esteban Suarez Cardona, Holger Boche, Gitta Kutyniok

CoRR, October, 2025

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

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

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

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

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

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

CoRR, 2023

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