36 citations · 64 across the 7 of their papers we have counts for
8 papers
Noise Propagation and Effective Channels in Encrypted Quantum Cloning
Yun-Fei Li, Fei-Hao Yang, Qing Chen +1
We investigate noise propagation in encrypted quantum cloning by formulating the protocol in terms of effective recovery channels. For local single-qubit completely positive trace-…
H3PC: Hypersonic, High-Order, High-Performance Code with Adaptive Mesh Refinement and Real Chemistry
Ahmad Peyvan, Khemraj Shukla, George Em Karniadakis
We have developed a hypersonic high-order, high-performance code (HPC) utilizing the ``Trixi.jl" framework in order to simulate both non-reactive and chemically reactive compre…
Crack Path Prediction with Operator Learning using Discrete Particle System data Generation
Elham Kiyani, Venkatesh Ananchaperumal, Ahmad Peyvan +3
Accurately modeling crack propagation is critical for predicting failure in engineering materials and structures, where small cracks can rapidly evolve and cause catastrophic damag…
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…
Transformers as Neural Operators for Solutions of Differential Equations with Finite Regularity
Benjamin Shih, Ahmad Peyvan, Zhongqiang Zhang +1
Neural operator learning models have emerged as very effective surrogates in data-driven methods for partial differential equations (PDEs) across different applications from comput…
RiemannONets: Interpretable Neural Operators for Riemann Problems
Ahmad Peyvan, Vivek Oommen, Ameya D. Jagtap +1
Developing the proper representations for simulating high-speed flows with strong shock waves, rarefactions, and contact discontinuities has been a long-standing question in numeri…
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…
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…