241 citations · 925 across the 11 of their papers we have counts for
4 papers · 1 filter
Error Estimates and Physics Informed Augmentation of Neural Networks for Thermally Coupled Incompressible Navier Stokes Equations
Shoaib Goraya, Nahil Sobh, Arif Masud
Physics Informed Neural Networks (PINNs) are shown to be a promising method for the approximation of Partial Differential Equations (PDEs). PINNs approximate the PDE solution by mi…
Continual Learning for Tumor Classification in Histopathology Images
Veena Kaustaban, Qinle Ba, Ipshita Bhattacharya +6
Recent years have seen great advancements in the development of deep learning models for histopathology image analysis in digital pathology applications, evidenced by the increasin…
Enhanced physics-informed neural networks for hyperelasticity
Diab W. Abueidda, Seid Koric, Erman Guleryuz +1
Physics-informed neural networks have gained growing interest. Specifically, they are used to solve partial differential equations governing several physical phenomena. However, ph…
A deep learning energy method for hyperelasticity and viscoelasticity
Diab W. Abueidda, Seid Koric, Rashid Abu Al-Rub +3
The potential energy formulation and deep learning are merged to solve partial differential equations governing the deformation in hyperelastic and viscoelastic materials. The pres…