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

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

2 February 2023

Michael Penwarden

Robert M. Kirby

George Karniadakis

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

12 / 12 papers shown

Title
State-space models are accurate and efficient neural operators for dynamical systems Zheyuan Hu Nazanin Ahmadi Daryakenari Qianli Shen Kenji Kawaguchi George Karniadakis Mamba AI4CE 219 17 0 28 Jan 2025
Reliable extrapolation of deep neural operators informed by physics or sparse observations Min Zhu Handi Zhang Anran Jiao George Karniadakis Lu Lu 102 100 0 13 Dec 2022
NeuralUQ: A comprehensive library for uncertainty quantification in neural differential equations and operators Zongren Zou Xuhui Meng Apostolos F. Psaros George Karniadakis AI4CE 97 39 0 25 Aug 2022
Multifidelity Deep Operator Networks For Data-Driven and Physics-Informed Problems Amanda A. Howard M. Perego G. Karniadakis P. Stinis AI4CE 97 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
Bi-fidelity Modeling of Uncertain and Partially Unknown Systems using DeepONets Subhayan De Matthew J. Reynolds M. Hassanaly Ryan N. King Alireza Doostan AI4CE 93 38 0 03 Apr 2022
On the influence of over-parameterization in manifold based surrogates and deep neural operators Katiana Kontolati S. Goswami Michael D. Shields George Karniadakis 65 42 0 09 Mar 2022
MIONet: Learning multiple-input operators via tensor product Pengzhan Jin Shuai Meng Lu Lu 93 174 0 12 Feb 2022
Enhanced data efficiency using deep neural networks and Gaussian processes for aerodynamic design optimization Sudharshan Ashwin Renganathan Romit Maulik and J. Ahuja 31 73 0 15 Aug 2020
Neural Operator: Graph Kernel Network for Partial Differential Equations Zong-Yi Li Nikola B. Kovachki Kamyar Azizzadenesheli Burigede Liu K. Bhattacharya Andrew M. Stuart Anima Anandkumar 208 749 0 07 Mar 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
Automatic differentiation in machine learning: a survey A. G. Baydin Barak A. Pearlmutter Alexey Radul J. Siskind PINN AI4CE ODL 181 2,824 0 20 Feb 2015