An accelerated hybrid data-driven/model-based approach for poroelasticity problems with multi-fidelity multi-physics data

30 November 2020

Papers citing "An accelerated hybrid data-driven/model-based approach for poroelasticity problems with multi-fidelity multi-physics data"

6 / 6 papers shown

Title
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 84 43 0 06 May 2024
A physics-informed deep neural network for surrogate modeling in classical elasto-plasticity M. Eghbalian M. Pouragha R. Wan AI4CE PINN 34 47 0 26 Apr 2022
Manifold embedding data-driven mechanics B. Bahmani WaiChing Sun PINN AI4CE 68 9 0 18 Dec 2021
A Physics Informed Neural Network Approach to Solution and Identification of Biharmonic Equations of Elasticity M. Vahab E. Haghighat M. Khaleghi N. Khalili PINN 120 45 0 16 Aug 2021
Data-driven discovery of interpretable causal relations for deep learning material laws with uncertainty propagation Xiao Sun B. Bahmani Nikolaos N. Vlassis WaiChing Sun Yanxun Xu CML AI4CE 116 26 0 20 May 2021
Billion-scale similarity search with GPUs Jeff Johnson Matthijs Douze Hervé Jégou 383 3,750 0 28 Feb 2017