833 citations · 1.1k across the 10 of their papers we have counts for
12 papers
Site-scale, Multi-resolution WRF Wind Modeling with Observational Nudging for Methane Emission Monitoring in the Permian Basin
Ehsan Kharazmi, Erin Menezes, George Karniadakis +2
Accurate methane source localization and emission-rate estimation at oil and gas facilities require wind fields that resolve site-scale spatial and temporal variability, which cann…
DeepVIVONet: Using deep neural operators to optimize sensor locations with application to vortex-induced vibrations
Ruyin Wan, Ehsan Kharazmi, Michael S Triantafyllou +1
We introduce DeepVIVONet, a new framework for optimal dynamic reconstruction and forecasting of the vortex-induced vibrations (VIV) of a marine riser, using field data. We demonstr…
Fractional Buffer Layers: Absorbing Boundary Conditions for Wave Propagation
Min Cai, Ehsan Kharazmi, Changpin Li +1
We develop fractional buffer layers (FBLs) to absorb propagating waves without reflection in bounded domains. Our formulation is based on variable-order spatial fractional derivati…
Anomalous Nonlinear Dynamics Behavior of Fractional Viscoelastic Structures
Jorge L. Suzuki, Pegah Varghaei, Ehsan Kharazmi +1
Fractional models and their parameters are sensitive to changes in the intrinsic micro-structures of anomalous materials. We investigate how such physics-informed models propagate…
hp-VPINNs: Variational Physics-Informed Neural Networks With Domain Decomposition
Ehsan Kharazmi, Zhongqiang Zhang, George Em Karniadakis
We formulate a general framework for hp-variational physics-informed neural networks (hp-VPINNs) based on the nonlinear approximation of shallow and deep neural networks and hp-ref…
Variational Physics-Informed Neural Networks For Solving Partial Differential Equations
E. Kharazmi, Z. Zhang, G. E. Karniadakis
Physics-informed neural networks (PINNs) [31] use automatic differentiation to solve partial differential equations (PDEs) by penalizing the PDE in the loss function at a random se…