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ELPINN: Eulerian Lagrangian Physics-Informed Neural Network
Sukirt Thakur, Maziar Raissi
Physics-Informed Neural Networks (PINNs) have gained widespread popularity for solving inverse and forward problems across a range of scientific and engineering domains. However, m…
Temporal Consistency Loss for Physics-Informed Neural Networks
Sukirt Thakur, Maziar Raissi, Harsa Mitra +1
Physics-informed neural networks (PINNs) have been widely used to solve partial differential equations in a forward and inverse manner using deep neural networks. However, training…
ViscoelasticNet: A physics informed neural network framework for stress discovery and model selection
Sukirt Thakur, Maziar Raissi, Arezoo M. Ardekani
Viscoelastic fluids are a class of fluids that exhibit both viscous and elastic nature. Modelling such fluids requires constitutive equations for the stress, and choosing the most…
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…