DeepONet-Grid-UQ: A Trustworthy Deep Operator Framework for Predicting the Power Grid's Post-Fault Trajectories

15 February 2022

Papers citing "DeepONet-Grid-UQ: A Trustworthy Deep Operator Framework for Predicting the Power Grid's Post-Fault Trajectories"

25 / 25 papers shown

Title
Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning Amirhossein Mollaali Christian Moya Amanda A. Howard Alexander Heinlein P. Stinis Guang Lin 28 0 0 21 Apr 2025
ON-Traffic: An Operator Learning Framework for Online Traffic Flow Estimation and Uncertainty Quantification from Lagrangian Sensors Jake Rap Amritam Das 64 0 0 18 Mar 2025
Conformalized Prediction of Post-Fault Voltage Trajectories Using Pre-trained and Finetuned Attention-Driven Neural Operators Amirhossein Mollaali Gabriel Zufferey Gonzalo E. Constante-Flores Christian Moya Can Li Guang Lin Meng Yue 44 0 0 31 Oct 2024
A Unified Approach for Learning the Dynamics of Power System Generators and Inverter-based Resources Shaohui Liu Weiqian Cai Hao Zhu Brian Johnson 29 0 0 22 Sep 2024
Physics and geometry informed neural operator network with application to acoustic scattering S. Nair Timothy F. Walsh Greg Pickrell Fabio Semperlotti AI4CE 38 2 0 02 Jun 2024
Uncertainty quantification for deeponets with ensemble kalman inversion Andrew Pensoneault Xueyu Zhu 26 1 0 06 Mar 2024
Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks Christian Moya Amirhossein Mollaali Zecheng Zhang Lu Lu Guang Lin UQCV 49 17 0 23 Feb 2024
RiemannONets: Interpretable Neural Operators for Riemann Problems Ahmad Peyvan Vivek Oommen Ameya Dilip Jagtap George Karniadakis AI4CE 38 22 0 16 Jan 2024
A Mathematical Guide to Operator Learning Nicolas Boullé Alex Townsend 34 36 0 22 Dec 2023
B-LSTM-MIONet: Bayesian LSTM-based Neural Operators for Learning the Response of Complex Dynamical Systems to Length-Variant Multiple Input Functions Zhihao Kong Amirhossein Mollaali Christian Moya Na Lu Guang Lin 21 2 0 28 Nov 2023
Uncertainty quantification for noisy inputs-outputs in physics-informed neural networks and neural operators Zongren Zou Xuhui Meng George Karniadakis AI4CE 35 19 0 19 Nov 2023
A Physics-Guided Bi-Fidelity Fourier-Featured Operator Learning Framework for Predicting Time Evolution of Drag and Lift Coefficients Amirhossein Mollaali Izzet Sahin Iqrar Raza Christian Moya Guillermo Paniagua Guang Lin 21 2 0 07 Nov 2023
D2NO: Efficient Handling of Heterogeneous Input Function Spaces with Distributed Deep Neural Operators Zecheng Zhang Christian Moya Lu Lu Guang Lin Hayden Schaeffer 29 11 0 29 Oct 2023
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 14 16 0 01 Jun 2023
IB-UQ: Information bottleneck based uncertainty quantification for neural function regression and neural operator learning Ling Guo Hao Wu Wenwen Zhou Yan Wang Tao Zhou UQCV 15 11 0 07 Feb 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 32 8 0 29 Jan 2023
Reliable extrapolation of deep neural operators informed by physics or sparse observations Min Zhu Handi Zhang Anran Jiao George Karniadakis Lu Lu 44 91 0 13 Dec 2022
Deep Learning Methods for Partial Differential Equations and Related Parameter Identification Problems Derick Nganyu Tanyu Jianfeng Ning Tom Freudenberg Nick Heilenkötter A. Rademacher U. Iben Peter Maass AI4CE 23 34 0 06 Dec 2022
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 50 9 0 21 Sep 2022
Variational Bayes Deep Operator Network: A data-driven Bayesian solver for parametric differential equations Shailesh Garg S. Chakraborty 29 6 0 12 Jun 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 18 102 0 14 Apr 2022
Scalable Uncertainty Quantification for Deep Operator Networks using Randomized Priors Yibo Yang Georgios Kissas P. Perdikaris BDL UQCV 32 40 0 06 Mar 2022
DAE-PINN: A Physics-Informed Neural Network Model for Simulating Differential-Algebraic Equations with Application to Power Networks Christian Moya Guang Lin AI4CE PINN 56 37 0 09 Sep 2021
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data Liu Yang Xuhui Meng George Karniadakis PINN 183 760 0 13 Mar 2020
MCMC using Hamiltonian dynamics Radford M. Neal 185 3,266 0 09 Jun 2012