1.4k citations · 1.4k across the 3 of their papers we have counts for
10 papers
Physics-Informed Neural Networks and Extensions
Maziar Raissi, Paris Perdikaris, Nazanin Ahmadi +1
In this paper, we review the new method Physics-Informed Neural Networks (PINNs) that has become the main pillar in scientific machine learning, we present recent practical extensi…
A deep learning framework for solution and discovery in solid mechanics
Ehsan Haghighat, Maziar Raissi, Adrian Moure +2
We present the application of a class of deep learning, known as Physics Informed Neural Networks (PINN), to learning and discovery in solid mechanics. We explain how to incorporat…
Deep Learning of Turbulent Scalar Mixing
Maziar Raissi, Hessam Babaee, Peyman Givi
Based on recent developments in physics-informed deep learning and deep hidden physics models, we put forth a framework for discovering turbulence models from scattered and potenti…
Deep Learning of Vortex Induced Vibrations
Maziar Raissi, Zhicheng Wang, Michael S. Triantafyllou +1
Vortex induced vibrations of bluff bodies occur when the vortex shedding frequency is close to the natural frequency of the structure. Of interest is the prediction of the lift and…
Hidden Fluid Mechanics: A Navier-Stokes Informed Deep Learning Framework for Assimilating Flow Visualization Data
Maziar Raissi, Alireza Yazdani, George Em Karniadakis
We present hidden fluid mechanics (HFM), a physics informed deep learning framework capable of encoding an important class of physical laws governing fluid motions, namely the Navi…
Machine Learning of Space-Fractional Differential Equations
Mamikon Gulian, Maziar Raissi, Paris Perdikaris +1
Data-driven discovery of "hidden physics" -- i.e., machine learning of differential equation models underlying observed data -- has recently been approached by embedding the discov…