124 citations · 269 across the 17 of their papers we have counts for
19 papers
Operator Learning with Variational Quantum Circuits
Jorgen Wu, Masoud Barati, Peyman Givi
A new methodology is developed for quantum machine learning which enables variational quantum circuits to learn linear and non-linear solution operators to differential equations.…
A priori Assessment of Tensor-Network Encoding for Isotropic Turbulent Flows
Massen Esmaeili, Hirad Alipanah, Robert Pinkston +5
Tensor networks (TNs), originally developed for simulating many-body quantum systems, provide a systematic framework for approximating high-dimensional fields. This is achieved by…
TIDE: A Physically Diverse 3D Turbulence Benchmark Dataset for Advancing Scientific Machine Learning
Yilong Dai, Yiming Sun, Yiheng Chen +4
Turbulence is a central testbed for machine learning on physical dynamics because its governing laws are known exactly. However, most existing studies remain in 2D, while 3D turbul…
PEST: Physics-Enhanced Swin Transformer for 3D Turbulence Simulation
Yilong Dai, Shengyu Chen, Xiaowei Jia +2
Accurate simulation of turbulent flows is fundamental to scientific and engineering applications. Direct numerical simulation (DNS) offers the highest fidelity but is computational…
Matrix Product State Simulation of Reacting Shear Flows
Robert Pinkston, Nikita Gourianov, Hirad Alipanah +3
Direct numerical simulation (DNS) of turbulent reactive flows has been the subject of significant research interest for several decades. Accurate prediction of the effects of turbu…
Provably Efficient Quantum Algorithms for Solving Nonlinear Differential Equations Using Multiple Bosonic Modes Coupled with Qubits
Yu Gan, Hirad Alipanah, Jinglei Cheng +7
Quantum computers have long been expected to efficiently solve complex classical differential equations. Most digital, fault-tolerant approaches use Carleman linearization to map n…
Quantum Dynamics Simulation of the Advection-Diffusion Equation
Hirad Alipanah, Feng Zhang, Yongxin Yao +6
The advection-diffusion equation is simulated on a superconducting quantum computer via several quantum algorithms. Three formulations are considered: (1) Trotterization, (2) varia…
Modeling Continuous Spatial-temporal Dynamics of Turbulent Flow with Test-time Refinement
Shengyu Chen, Peyman Givi, Can Zheng +1
The precise simulation of turbulent flows holds immense significance across various scientific and engineering domains, including climate science, freshwater science, and energy-ef…
Tensor networks enable the calculation of turbulence probability distributions
Nikita Gourianov, Peyman Givi, Dieter Jaksch +1
Predicting the dynamics of turbulent fluid flows has long been a central goal of science and engineering. Yet, even with modern computing technology, accurate simulation of all but…
Physics-enhanced Neural Operator for Simulating Turbulent Transport
Shengyu Chen, Peyman Givi, Can Zheng +1
The precise simulation of turbulent flows is of immense importance in a variety of scientific and engineering fields, including climate science, freshwater science, and the develop…
Reconstructing Turbulent Flows Using Physics-Aware Spatio-Temporal Dynamics and Test-Time Refinement
Shengyu Chen, Tianshu Bao, Peyman Givi +2
Simulating turbulence is critical for many societally important applications in aerospace engineering, environmental science, the energy industry, and biomedicine. Large eddy simul…
Variational Quantum Algorithms for Computational Fluid Dynamics
Dieter Jaksch, Peyman Givi, Andrew J. Daley +1
Quantum computing uses the physical principles of very small systems to develop computing platforms which can solve problems that are intractable on conventional supercomputers. Th…
Reduced Order Modeling of Turbulence-Chemistry Interactions using Dynamically Bi-Orthonormal Decomposition
Aidyn Aitzhan, Arash G. Nouri, Peyman Givi +1
The performance of the dynamically bi-orthogonal (DBO) decomposition for the reduced order modeling of turbulence-chemistry interactions is assessed. DBO is an on-the-fly low-rank…
PeleLM-FDF Large Eddy Simulator of Turbulent Combustion
Aidyn Aitzhan, Shervin Sammak, Peyman Givi +1
A new computational methodology, termed "PeleLM-FDF" is developed and utilized for high fidelity large eddy simulation (LES) of complex turbulent combustion systems. This methodolo…
Skeletal Reaction Models for Methane Combustion
Yinmin Liu, Hessam Babaee, Peyman Givi +3
A local-sensitivity-analysis technique is employed to generate new skeletal reaction models for methane combustion from the foundational fuel chemistry model (FFCM-1). The sensitiv…
Reconstructing High-resolution Turbulent Flows Using Physics-Guided Neural Networks
Shengyu Chen, Shervin Sammak, Peyman Givi +2
Direct numerical simulation (DNS) of turbulent flows is computationally expensive and cannot be applied to flows with large Reynolds numbers. Large eddy simulation (LES) is an alte…
A Quantum Inspired Approach to Exploit Turbulence Structures
Nikita Gourianov, Michael Lubasch, Sergey Dolgov +5
Understanding turbulence is the key to our comprehension of many natural and technological flow processes. At the heart of this phenomenon lies its intricate multi-scale nature, de…
Skeletal Model Reduction with Forced Optimally Time Dependent Modes
A. G. Nouri, H. Babaee, P. Givi +2
Sensitivity analysis with forced optimally time dependent (f-OTD) modes is introduced and its application for skeletal model reduction is demonstrated. f-OTD expands the sensitivit…
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…