Calibrate, Emulate, Sample

10 January 2020

Papers citing "Calibrate, Emulate, Sample"

29 / 29 papers shown

Title
Efficient Deconvolution in Populational Inverse Problems Arnaud Vadeboncoeur Mark Girolami Andrew M. Stuart 42 0 0 26 May 2025
A survey of Monte Carlo methods for noisy and costly densities with application to reinforcement learning and ABC F. Llorente Luca Martino Jesse Read D. Delgado OffRL 165 14 0 03 Jan 2025
A Primer on Variational Inference for Physics-Informed Deep Generative Modelling Alex Glyn-Davies Arnaud Vadeboncoeur O. Deniz Akyildiz Ieva Kazlauskaite Mark Girolami PINN 139 0 0 10 Sep 2024
An adaptive Gaussian process method for multi-modal Bayesian inverse problems Zhihang Xu Xiaoyu Zhu Daoji Li Qifeng Liao 23 0 0 05 Sep 2024
Hyperparameter Optimization for Randomized Algorithms: A Case Study on Random Features Oliver R. A. Dunbar Nicholas H. Nelsen Maya Mutic 147 7 0 30 Jun 2024
FUSE: Fast Unified Simulation and Estimation for PDEs Levi E. Lingsch Dana Grund Siddhartha Mishra Georgios Kissas AI4CE 93 2 0 23 May 2024
Enhancing Gaussian Process Surrogates for Optimization and Posterior Approximation via Random Exploration Hwanwoo Kim D. Sanz-Alonso 79 3 0 30 Jan 2024
Learning About Structural Errors in Models of Complex Dynamical Systems Jin-Long Wu Matthew E. Levine Tapio Schneider Andrew M. Stuart AI4CE 82 18 0 29 Dec 2023
Scaling Up Bayesian Neural Networks with Neural Networks Zahra Moslemi Yang Meng Shiwei Lan Babak Shahbaba BDL 73 1 0 19 Dec 2023
Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference Philipp Reiser Javier Enrique Aguilar A. Guthke Paul-Christian Bürkner 151 3 0 08 Dec 2023
Next-Generation Earth System Models: Towards Reliable Hybrid Models for Weather and Climate Applications Tom Beucler Erwan Koch Sven Kotlarski D. Leutwyler Adrien Michel Jonathan Koh AI4Cl 35 2 0 22 Nov 2023
Analysis of a Computational Framework for Bayesian Inverse Problems: Ensemble Kalman Updates and MAP Estimators Under Mesh Refinement D. Sanz-Alonso Nathan Waniorek 83 5 0 19 Apr 2023
Solving High-Dimensional Inverse Problems with Auxiliary Uncertainty via Operator Learning with Limited Data Joseph L. Hart Mamikon A. Gulian Indu Manickam L. Swiler 60 8 0 20 Mar 2023
Introduction To Gaussian Process Regression In Bayesian Inverse Problems, With New ResultsOn Experimental Design For Weighted Error Measures T. Helin Andrew M. Stuart A. Teckentrup K. Zygalakis 62 5 0 09 Feb 2023
Embed and Emulate: Learning to estimate parameters of dynamical systems with uncertainty quantification Ruoxi Jiang Rebecca Willett 61 7 0 03 Nov 2022
Semi-automatic tuning of coupled climate models with multiple intrinsic timescales: lessons learned from the Lorenz96 model Redouane Lguensat Julie Deshayes Homer Durand Venkatramani Balaji 96 4 0 11 Aug 2022
Randomized Maximum Likelihood via High-Dimensional Bayesian Optimization Valentin Breaz Richard D. Wilkinson 65 0 0 17 Apr 2022
Localization in Ensemble Kalman inversion Xin T. Tong Matthias Morzfeld 89 21 0 26 Jan 2022
An efficient estimation of time-varying parameters of dynamic models by combining offline batch optimization and online data assimilation Y. Sawada 46 2 0 24 Oct 2021
Combining machine learning and data assimilation to forecast dynamical systems from noisy partial observations Georg Gottwald Sebastian Reich AI4CE 94 38 0 08 Aug 2021
Bridging observation, theory and numerical simulation of the ocean using Machine Learning Maike Sonnewald Redouane Lguensat Daniel C. Jones P. Dueben J. Brajard Venkatramani Balaji AI4Cl AI4CE 96 101 0 26 Apr 2021
Approximate Bayesian inference from noisy likelihoods with Gaussian process emulated MCMC Marko Jarvenpaa J. Corander 61 5 0 08 Apr 2021
Ensemble Inference Methods for Models With Noisy and Expensive Likelihoods Oliver R. A. Dunbar Andrew B. Duncan Andrew M. Stuart Marie-Therese Wolfram 82 27 0 07 Apr 2021
Scaling Up Bayesian Uncertainty Quantification for Inverse Problems using Deep Neural Networks Shiwei Lan Shuyi Li Babak Shahbaba UQCV BDL 84 16 0 11 Jan 2021
Calibration and Uncertainty Quantification of Convective Parameters in an Idealized GCM Oliver R. A. Dunbar A. Garbuno-Iñigo T. Schneider Andrew M. Stuart 86 60 0 24 Dec 2020
Deep Importance Sampling based on Regression for Model Inversion and Emulation F. Llorente Luca Martino D. Delgado G. Camps-Valls 86 19 0 20 Oct 2020
Kernel-based parameter estimation of dynamical systems with unknown observation functions Ofir Lindenbaum A. Sagiv Zhengchao Wan Ronen Talmon 40 5 0 09 Sep 2020
Probabilistic Gradients for Fast Calibration of Differential Equation Models Jon Cockayne Andrew B. Duncan 63 5 0 03 Sep 2020
Learning Stochastic Closures Using Ensemble Kalman Inversion T. Schneider Andrew M. Stuart Jin-Long Wu 101 40 0 17 Apr 2020