Replacing Automatic Differentiation by Sobolev Cubatures fastens Physics Informed Neural Nets and strengthens their Approximation Power

23 November 2022

Papers citing "Replacing Automatic Differentiation by Sobolev Cubatures fastens Physics Informed Neural Nets and strengthens their Approximation Power"

5 / 5 papers shown

Title
Zero Coordinate Shift: Whetted Automatic Differentiation for Physics-informed Operator Learning Kuangdai Leng Mallikarjun Shankar Jeyan Thiyagalingam 26 2 0 01 Nov 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 14 1 0 15 Sep 2023
Continuous Spatiotemporal Transformers Antonio H. O. Fonseca E. Zappala J. O. Caro David van Dijk 23 7 0 31 Jan 2023
Learning Partial Differential Equations by Spectral Approximates of General Sobolev Spaces Juan Esteban Suarez Cardona Phil-Alexander Hofmann Michael Hecht 13 2 0 12 Jan 2023
hp-VPINNs: Variational Physics-Informed Neural Networks With Domain Decomposition E. Kharazmi Zhongqiang Zhang George Karniadakis 125 508 0 11 Mar 2020