activity
20192025
most citedPhysics-informed Neural Networks (PINNs) for Wave Propagation and Full Waveform Inversions

440 citations · 851 across the 27 of their papers we have counts for

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Showing physics.flu-dynShow all

6 papers · 1 filter

physics.flu-dyn2025

GIMLET: Generalizable and Interpretable Model Learning through Embedded Thermodynamics

Suguru Shiratori, Elham Kiyani, Khemraj Shukla +1

We develop a data-driven framework for discovering constitutive relations in models of fluid flow and scalar transport. Under the assumption that velocity and/or scalar fields are…

physics.flu-dyn2025

Body-Free Simulation of Three-Dimensional Turbulent Cylinder Wakes

Zhicheng Wang, Theo Käufer, Khemraj Shukla +2

We present a body-free simulation framework for three-dimensional turbulent cylinder wakes, in which the upstream cylinder is not explicitly resolved. Instead, the incompressible N…

physics.flu-dyn2024

Deep operator learning-based surrogate models for aerothermodynamic analysis of AEDC hypersonic waverider

Khemraj Shukla, Jasmine Ratchford, Luis Bravo +4

Neural networks are universal approximators that traditionally have been used to learn a map between function inputs and outputs. However, recent research has demonstrated that dee…

physics.flu-dyn2023★ 2 cited

Characterization of partial wetting by CMAS droplets using multiphase many-body dissipative particle dynamics and data-driven discovery based on PINNs

Elham Kiyani, Mahdi Kooshkbaghi, Khemraj Shukla +6

The molten sand, a mixture of calcia, magnesia, alumina, and silicate, known as CMAS, is characterized by its high viscosity, density, and surface tension. The unique properties of…

physics.flu-dyn2023★ 3 cited

Deep neural operators can serve as accurate surrogates for shape optimization: A case study for airfoils

Khemraj Shukla, Vivek Oommen, Ahmad Peyvan +5

Deep neural operators, such as DeepONets, have changed the paradigm in high-dimensional nonlinear regression from function regression to (differential) operator regression, paving…

physics.flu-dyn2022★ 24 cited

High-Order Methods for Hypersonic Flows with Strong Shocks and Real Chemistry

Ahmad Peyvan, Khemraj Shukla, Jesse Chan +1

We compare high-order methods including spectral difference (SD), flux reconstruction (FR), the entropy-stable discontinuous Galerkin spectral element method (ES-DGSEM), modal disc…