Juan Esteban Suarez Cardona

According to our database, Juan Esteban Suarez Cardona authored at least 4 papers between 2022 and 2023.

Collaborative distances:

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

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

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

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

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