A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges

16 June 2020

Papers citing "A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges"

46 / 46 papers shown

Title
Stochastic Process Learning via Operator Flow Matching Yaozhong Shi Zachary E. Ross D. Asimaki Kamyar Azizzadenesheli 62 2 0 10 Jan 2025
Measurements with Noise: Bayesian Optimization for Co-optimizing Noise and Property Discovery in Automated Experiments Boris N. Slautin Yu Liu Jan Dec Vladimir V. Shvartsman Doru C. Lupascu M. Ziatdinov Sergei V. Kalinin 42 0 0 03 Oct 2024
SPINEX-TimeSeries: Similarity-based Predictions with Explainable Neighbors Exploration for Time Series and Forecasting Problems Ahmed Z Naser M. Z. Naser AI4TS 21 0 0 04 Aug 2024
Multi-physics Simulation Guided Generative Diffusion Models with Applications in Fluid and Heat Dynamics Naichen Shi Hao Yan Shenghan Guo Raed Al Kontar DiffM AI4CE 43 0 0 25 Jul 2024
Gaussian Measures Conditioned on Nonlinear Observations: Consistency, MAP Estimators, and Simulation Yifan Chen Bamdad Hosseini H. Owhadi Andrew M. Stuart 79 1 0 21 May 2024
Neural Operator induced Gaussian Process framework for probabilistic solution of parametric partial differential equations Sawan Kumar R. Nayek Souvik Chakraborty 45 2 0 24 Apr 2024
Universal Functional Regression with Neural Operator Flows Yaozhong Shi Angela F. Gao Zachary E. Ross Kamyar Azizzadenesheli 50 4 0 03 Apr 2024
Gaussian Process Regression with Soft Inequality and Monotonicity Constraints Didem Kochan Xiu Yang 29 0 0 03 Apr 2024
Physics-constrained polynomial chaos expansion for scientific machine learning and uncertainty quantification Himanshu Sharma Lukávs Novák Michael D. Shields AI4CE 55 4 0 23 Feb 2024
PINN-BO: A Black-box Optimization Algorithm using Physics-Informed Neural Networks Dat Phan-Trong Hung The Tran A. Shilton Sunil R. Gupta 49 0 0 05 Feb 2024
Asymptotic properties of Vecchia approximation for Gaussian processes Myeongjong Kang Florian Schafer J. Guinness Matthias Katzfuss 40 5 0 29 Jan 2024
Deep Bayesian Reinforcement Learning for Spacecraft Proximity Maneuvers and Docking Desong Du Naiming Qi Yanfang Liu Wei Pan 16 0 0 07 Nov 2023
Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics J. Fuhg Reese E. Jones N. Bouklas AI4CE 39 23 0 05 Oct 2023
Enhanced Human-Robot Collaboration using Constrained Probabilistic Human-Motion Prediction Aadi Kothari Tony Tohme Xiaotong Zhang Kamal Youcef-Toumi 3DH 45 7 0 05 Oct 2023
A spectrum of physics-informed Gaussian processes for regression in engineering E. Cross T. Rogers D. J. Pitchforth S. Gibson Matthew R. Jones 29 8 0 19 Sep 2023
Physics-Informed Polynomial Chaos Expansions Lukávs Novák Himanshu Sharma Michael D. Shields 30 16 0 04 Sep 2023
Index-aware learning of circuits I. C. Garcia Peter Förster Lennart Jansen W. Schilders Sebastian Schöps 11 0 0 02 Sep 2023
Learning thermodynamically constrained equations of state with uncertainty Himanshu Sharma J. Gaffney Dimitrios Tsapetis Michael D. Shields 16 5 0 29 Jun 2023
SEAL: Simultaneous Exploration and Localization in Multi-Robot Systems Ehsan Latif Ramviyas Parasuraman 17 9 0 22 Jun 2023
A machine learning approach to the prediction of heat-transfer coefficients in micro-channels Tullio Traverso F. Coletti Luca Magri T. Karayiannis Omar K. Matar 8 0 0 28 May 2023
Gaussian Processes with State-Dependent Noise for Stochastic Control Marcel Menner K. Berntorp 24 3 0 25 May 2023
Stochastic PDE representation of random fields for large-scale Gaussian process regression and statistical finite element analysis Kim Jie Koh F. Cirak AI4CE 34 9 0 23 May 2023
UQpy v4.1: Uncertainty Quantification with Python Dimitrios Tsapetis Michael D. Shields Dimitris G. Giovanis Audrey Olivier Lukás Novák ... Mohit Chauhan Katiana Kontolati Lohit Vandanapu Dimitrios Loukrezis Michael Gardner GP 29 11 0 16 May 2023
Error Analysis of Kernel/GP Methods for Nonlinear and Parametric PDEs Pau Batlle Yifan Chen Bamdad Hosseini H. Owhadi Andrew M. Stuart 39 17 0 08 May 2023
Stochastic Cell Transmission Models of Traffic Networks Zachary Feinstein M. Kleiber Stefan Weber 11 1 0 23 Apr 2023
Parameter Inference based on Gaussian Processes Informed by Nonlinear Partial Differential Equations Zhao-Xia Li Shih-Feng Yang Jeff Wu 30 2 0 22 Dec 2022
A correlated pseudo-marginal approach to doubly intractable problems Yu Yang M. Quiroz Robert Kohn Scott A. Sisson 26 1 0 06 Oct 2022
Physically Meaningful Uncertainty Quantification in Probabilistic Wind Turbine Power Curve Models as a Damage Sensitive Feature J. H. Mclean Matthew R. Jones Brandon J. O'Connell Eoghan Maguire T. Rogers 32 6 0 30 Sep 2022
A connection between probability, physics and neural networks Sascha Ranftl PINN 22 9 0 26 Sep 2022
Monotonic Gaussian process for physics-constrained machine learning with materials science applications Anh Tran Kathryn A. Maupin T. Rodgers PINN AI4CE 31 6 0 31 Aug 2022
Constraining Gaussian processes for physics-informed acoustic emission mapping Matthew R. Jones T. Rogers E. Cross AI4CE 45 16 0 03 Jun 2022
Prediction for Distributional Outcomes in High-Performance Computing I/O Variability Li Xu Yili Hong M. Morris K. Cameron 17 0 0 19 May 2022
Data-aided Underwater Acoustic Ray Propagation Modeling Kexin Li M. Chitre 33 12 0 12 May 2022
Discrepancy Modeling Framework: Learning missing physics, modeling systematic residuals, and disambiguating between deterministic and random effects Megan R. Ebers K. Steele J. Nathan Kutz 54 15 0 10 Mar 2022
Incorporating Sum Constraints into Multitask Gaussian Processes Philipp Pilar Carl Jidling Thomas B. Schon Niklas Wahlström TPM 26 3 0 03 Feb 2022
A Kernel-Based Approach for Modelling Gaussian Processes with Functional Information J. Nicholson P. Kiessler D. Brown GP 19 3 0 26 Jan 2022
Structure-Preserving Learning Using Gaussian Processes and Variational Integrators Jan Brüdigam Martin Schuck A. Capone Stefan Sosnowski Sandra Hirche 19 4 0 10 Dec 2021
Stochastic Processes Under Linear Differential Constraints : Application to Gaussian Process Regression for the 3 Dimensional Free Space Wave Equation Iain Henderson P. Noble O. Roustant 26 1 0 23 Nov 2021
Practical, Provably-Correct Interactive Learning in the Realizable Setting: The Power of True Believers Julian Katz-Samuels Blake Mason Kevin Jamieson R. Nowak 16 0 0 09 Nov 2021
Hierarchical Non-Stationary Temporal Gaussian Processes With $L^1$ -Regularization Zheng Zhao Rui Gao Simo Särkkä 31 0 0 20 May 2021
Posterior contraction for deep Gaussian process priors G. Finocchio Johannes Schmidt-Hieber 63 11 0 16 May 2021
Solving and Learning Nonlinear PDEs with Gaussian Processes Yifan Chen Bamdad Hosseini H. Owhadi Andrew M. Stuart 42 153 0 24 Mar 2021
Advanced Stationary and Non-Stationary Kernel Designs for Domain-Aware Gaussian Processes M. Noack J. Sethian GP 6 22 0 05 Feb 2021
Gaussian Process Regression constrained by Boundary Value Problems Mamikon A. Gulian A. Frankel L. Swiler 11 25 0 22 Dec 2020
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems J. Willard X. Jia Shaoming Xu M. Steinbach Vipin Kumar AI4CE 91 392 0 10 Mar 2020
Local Gaussian process approximation for large computer experiments R. Gramacy D. Apley 129 392 0 02 Mar 2013