A Physics-Guided Bi-Fidelity Fourier-Featured Operator Learning
Framework for Predicting Time Evolution of Drag and Lift Coefficients

A Physics-Guided Bi-Fidelity Fourier-Featured Operator Learning Framework for Predicting Time Evolution of Drag and Lift Coefficients

7 November 2023

Amirhossein Mollaali

Guillermo Paniagua

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Papers citing "A Physics-Guided Bi-Fidelity Fourier-Featured Operator Learning Framework for Predicting Time Evolution of Drag and Lift Coefficients"

13 / 13 papers shown

Title
Deep Operator Learning-based Surrogate Models with Uncertainty Quantification for Optimizing Internal Cooling Channel Rib Profiles Izzet Sahin Christian Moya Amirhossein Mollaali Guang Lin Guillermo Paniagua AI4CE 70 16 0 01 Jun 2023
On Approximating the Dynamic Response of Synchronous Generators via Operator Learning: A Step Towards Building Deep Operator-based Power Grid Simulators Christian Moya Guang Lin Tianqiao Zhao Meng Yue 77 8 0 29 Jan 2023
DeepGraphONet: A Deep Graph Operator Network to Learn and Zero-shot Transfer the Dynamic Response of Networked Systems Yixuan Sun Christian Moya Guang Lin Meng Yue GNN 119 9 0 21 Sep 2022
Multifidelity Deep Operator Networks For Data-Driven and Physics-Informed Problems Amanda A. Howard M. Perego G. Karniadakis P. Stinis AI4CE 92 56 0 19 Apr 2022
Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport Lu Lu R. Pestourie Steven G. Johnson Giuseppe Romano AI4CE 81 108 0 14 Apr 2022
DeepONet-Grid-UQ: A Trustworthy Deep Operator Framework for Predicting the Power Grid's Post-Fault Trajectories Christian Moya Shiqi Zhang Meng Yue Guang Lin 71 43 0 15 Feb 2022
Neural Operator: Learning Maps Between Function Spaces Nikola B. Kovachki Zong-Yi Li Burigede Liu Kamyar Azizzadenesheli K. Bhattacharya Andrew M. Stuart Anima Anandkumar AI4CE 153 453 0 19 Aug 2021
A physics-informed variational DeepONet for predicting the crack path in brittle materials S. Goswami Minglang Yin Yue Yu G. Karniadakis AI4CE 72 197 0 16 Aug 2021
Physics-informed neural networks (PINNs) for fluid mechanics: A review Shengze Cai Zhiping Mao Zhicheng Wang Minglang Yin George Karniadakis PINN AI4CE 88 1,209 0 20 May 2021
Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets Sizhuang He Hanwen Wang P. Perdikaris AI4CE 102 710 0 19 Mar 2021
Fourier Neural Operator for Parametric Partial Differential Equations Zong-Yi Li Nikola B. Kovachki Kamyar Azizzadenesheli Burigede Liu K. Bhattacharya Andrew M. Stuart Anima Anandkumar AI4CE 516 2,456 0 18 Oct 2020
Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains Matthew Tancik Pratul P. Srinivasan B. Mildenhall Sara Fridovich-Keil N. Raghavan Utkarsh Singhal R. Ramamoorthi Jonathan T. Barron Ren Ng 135 2,450 0 18 Jun 2020
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators Lu Lu Pengzhan Jin George Karniadakis 248 2,162 0 08 Oct 2019