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ORCIDJuan Esteban Suarez Cardona
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2020 – today
- 2024
[j2]Chethan Krishnamurthy Ramanaik
, Anna Willmann, Juan Esteban Suarez Cardona, Pia Hanfeld, Nico Hoffmann, Michael Hecht:
Ensuring Topological Data-Structure Preservation under Autoencoder Compression Due to Latent Space Regularization in Gauss-Legendre Nodes. Axioms 13(8): 535 (2024)- [i4]Juan Esteban Suarez Cardona, Shashank Reddy, Michael Hecht:
Hybrid Surrogate Models: Circumventing Gibbs Phenomenon for Partial Differential Equations with Finite Shock-Type Discontinuities. CoRR abs/2408.02497 (2024) - 2023
- [j1]Juan Esteban Suarez Cardona, Michael Hecht:
Polynomial differentiation decreases the training time complexity of physics-informed neural networks and strengthens their approximation power. Mach. Learn. Sci. Technol. 4(4): 45005 (2023) - [i3]Juan Esteban Suarez Cardona, Phil-Alexander Hofmann, Michael Hecht:
Learning Partial Differential Equations by Spectral Approximates of General Sobolev Spaces. CoRR abs/2301.04887 (2023) - [i2]Chethan Krishnamurthy Ramanaik, Juan Esteban Suarez Cardona, Anna Willmann, Pia Hanfeld, Nico Hoffmann, Michael Hecht:
Ensuring Topological Data-Structure Preservation under Autoencoder Compression due to Latent Space Regularization in Gauss-Legendre nodes. CoRR abs/2309.08228 (2023) - 2022
- [i1]Juan Esteban Suarez Cardona, Michael Hecht:
Replacing Automatic Differentiation by Sobolev Cubatures fastens Physics Informed Neural Nets and strengthens their Approximation Power. CoRR abs/2211.15443 (2022)
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