427 citations · 563 across the 8 of their papers we have counts for
8 papers
Hypersonic Boundary Layer Transition and Heat Loading
Ahmad Peyvan, Luis Bravo, Anindya Ghoshal +2
Hypersonic boundary layer transition using high-order methods for direct numerical simulations (DNS) is largely unexplored, although a few references exist in the literature. Exper…
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…
Splitting physics-informed neural networks for inferring the dynamics of integer- and fractional-order neuron models
Simin Shekarpaz, Fanhai Zeng, George Karniadakis
We introduce a new approach for solving forward systems of differential equations using a combination of splitting methods and physics-informed neural networks (PINNs). The propose…
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…
Multifidelity Deep Operator Networks For Data-Driven and Physics-Informed Problems
Amanda A. Howard, Mauro Perego, George E. Karniadakis +1
Operator learning for complex nonlinear systems is increasingly common in modeling multi-physics and multi-scale systems. However, training such high-dimensional operators requires…
A physics-informed variational DeepONet for predicting the crack path in brittle materials
Somdatta Goswami, Minglang Yin, Yue Yu +1
Failure trajectories, identifying the probable failure zones, and damage statistics are some of the key quantities of relevance in brittle fracture applications. High-fidelity nume…
Theory and simulation of electrokinetic fluctuations in electrolyte solutions at the mesoscale
Mingge Deng, Faisal Tushar, Luis Bravo +3
Electrolyte solutions play an important role in energy storage devices, whose performance highly relies on the electrokinetic processes at sub-micron scales.\ Although fluctuations…
DeepONet prediction of linear instability waves in high-speed boundary layers
P. Clark Di Leoni, L. Lu, C. Meneveau +2
Deep operator networks (DeepONets) are trained to predict the linear amplification of instability waves in high-speed boundary layers and to perform data assimilation. In contrast…