Approximate Bayesian Neural Operators: Uncertainty Quantification for
Parametric PDEs

Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs

2 August 2022

Nicholas Kramer

Runa Eschenhagen

Lorenzo Rosasco

Papers citing "Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs"

12 / 12 papers shown

Title
Position: Epistemic Artificial Intelligence is Essential for Machine Learning Models to Know When They Do Not Know Shireen Kudukkil Manchingal Fabio Cuzzolin 56 0 0 08 May 2025
Learning Dual-Arm Coordination for Grasping Large Flat Objects Yongliang Wang H. Kasaei 34 0 0 04 Apr 2025
ON-Traffic: An Operator Learning Framework for Online Traffic Flow Estimation and Uncertainty Quantification from Lagrangian Sensors Jake Rap Amritam Das 67 0 0 18 Mar 2025
Linearization Turns Neural Operators into Function-Valued Gaussian Processes Emilia Magnani Marvin Pfortner Tobias Weber Philipp Hennig UQCV 69 1 0 07 Jun 2024
Neural Operator induced Gaussian Process framework for probabilistic solution of parametric partial differential equations Sawan Kumar R. Nayek Souvik Chakraborty 40 2 0 24 Apr 2024
Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs S. C. Mouli Danielle C. Maddix S. Alizadeh Gaurav Gupta Andrew Stuart Michael W. Mahoney Yuyang Wang UQCV AI4CE 48 2 0 15 Mar 2024
Learning Semilinear Neural Operators : A Unified Recursive Framework For Prediction And Data Assimilation Ashutosh Singh R. Borsoi Deniz Erdogmus Tales Imbiriba 49 0 0 24 Feb 2024
Calibrated Uncertainty Quantification for Operator Learning via Conformal Prediction Ziqi Ma Kamyar Azizzadenesheli A. Anandkumar 23 6 0 02 Feb 2024
Multiwavelet-based Operator Learning for Differential Equations Gaurav Gupta Xiongye Xiao P. Bogdan 126 200 0 28 Sep 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 232 2,287 0 18 Oct 2020
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam Mohammad Emtiyaz Khan Didrik Nielsen Voot Tangkaratt Wu Lin Y. Gal Akash Srivastava ODL 74 266 0 13 Jun 2018
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,138 0 06 Jun 2015