Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics

5 October 2023

Papers citing "Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics"

8 / 8 papers shown

Title
Input Specific Neural Networks Asghar A. Jadoon D. Thomas Seidl Reese E. Jones J. Fuhg PINN 50 0 0 01 Mar 2025
Input convex neural networks: universal approximation theorem and implementation for isotropic polyconvex hyperelastic energies Gian-Luca Geuken P. Kurzeja David Wiedemann J. Mosler 57 1 0 12 Feb 2025
Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models G. A. Padmanabha J. Fuhg C. Safta Reese E. Jones N. Bouklas 47 4 0 30 Jun 2024
A review on data-driven constitutive laws for solids J. Fuhg G. A. Padmanabha N. Bouklas B. Bahmani WaiChing Sun Nikolaos N. Vlassis Moritz Flaschel P. Carrara L. Lorenzis AI4CE AILaw 31 32 0 06 May 2024
Equivariant graph convolutional neural networks for the representation of homogenized anisotropic microstructural mechanical response Ravi G. Patel C. Safta Reese E. Jones AI4CE 40 1 0 05 Apr 2024
Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks Torsten Hoefler Dan Alistarh Tal Ben-Nun Nikoli Dryden Alexandra Peste MQ 141 684 0 31 Jan 2021
What is the State of Neural Network Pruning? Davis W. Blalock Jose Javier Gonzalez Ortiz Jonathan Frankle John Guttag 191 1,027 0 06 Mar 2020
Input Convex Neural Networks Brandon Amos Lei Xu J. Zico Kolter 187 601 0 22 Sep 2016