Linearly constrained Gaussian processes

2 March 2017

Papers citing "Linearly constrained Gaussian processes"

50 / 57 papers shown

Title
Plug-and-Play Physics-informed Learning using Uncertainty Quantified Port-Hamiltonian Models Kaiyuan Tan Peilun Li Jun Wang Thomas Beckers AI4CE 34 0 0 24 Apr 2025
Gaussian Process Priors for Boundary Value Problems of Linear Partial Differential Equations Jianle iHuang Marc Härkönen Markus Lange-Hegermann Bogdan Raiță 75 0 0 25 Nov 2024
Physics-Informed Variational State-Space Gaussian Processes Oliver Hamelijnck Arno Solin Theodoros Damoulas 36 0 0 20 Sep 2024
Physics-Constrained Learning for PDE Systems with Uncertainty Quantified Port-Hamiltonian Models Kaiyuan Tan Peilun Li Thomas Beckers AI4CE 28 3 0 17 Jun 2024
On the Laplace Approximation as Model Selection Criterion for Gaussian Processes Andreas Besginow J. D. Hüwel Thomas Pawellek Christian Beecks Markus Lange-Hegermann 23 0 0 14 Mar 2024
Physics-Informed Neural Networks with Hard Linear Equality Constraints Hao Chen Gonzalo E. Constante-Flores Canzhou Li PINN 21 11 0 11 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
Learning thermodynamically constrained equations of state with uncertainty Himanshu Sharma J. Gaffney Dimitrios Tsapetis Michael D. Shields 14 5 0 29 Jun 2023
Gaussian Processes with State-Dependent Noise for Stochastic Control Marcel Menner K. Berntorp 24 3 0 25 May 2023
Images of Gaussian and other stochastic processes under closed, densely-defined, unbounded linear operators T. Matsumoto T. Sullivan 38 3 0 05 May 2023
Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces Kalvik Jakkala Srinivas Akella 34 1 0 28 Feb 2023
Gaussian Process Priors for Systems of Linear Partial Differential Equations with Constant Coefficients Marc Härkönen Markus Lange-Hegermann Bogdan Raiță 37 15 0 29 Dec 2022
Nonlinear System Identification: Learning while respecting physical models using a sequential Monte Carlo method A. Wigren Johan Wågberg Fredrik Lindsten A. Wills Thomas B. Schon 26 10 0 26 Oct 2022
A Kernel Approach for PDE Discovery and Operator Learning D. Long Nicole Mrvaljević Shandian Zhe Bamdad Hosseini 31 7 0 14 Oct 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 to Systems of Linear Ordinary Differential Equations Andreas Besginow Markus Lange-Hegermann 37 11 0 26 Aug 2022
Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields Niklas Schmitz Klaus-Robert Muller Stefan Chmiela AI4CE 36 11 0 25 Aug 2022
Disintegration of Gaussian Measures for Sequential Assimilation of Linear Operator Data Cédric Travelletti D. Ginsbourger 19 5 0 27 Jul 2022
Learning differentiable solvers for systems with hard constraints Geoffrey Negiar Michael W. Mahoney Aditi S. Krishnapriyan 39 28 0 18 Jul 2022
Constraining Gaussian processes for physics-informed acoustic emission mapping Matthew R. Jones T. Rogers E. Cross AI4CE 30 16 0 03 Jun 2022
Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning Veit Wild Robert Hu Dino Sejdinovic BDL 56 11 0 12 May 2022
On boundary conditions parametrized by analytic functions Markus Lange-Hegermann D. Robertz 34 5 0 06 May 2022
Adjoint-aided inference of Gaussian process driven differential equations Paterne Gahungu Christopher W. Lanyon Mauricio A. Alvarez Engineer Bainomugisha M. Smith Richard D. Wilkinson 26 5 0 09 Feb 2022
Incorporating Sum Constraints into Multitask Gaussian Processes Philipp Pilar Carl Jidling Thomas B. Schon Niklas Wahlström TPM 24 3 0 03 Feb 2022
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 21 1 0 23 Nov 2021
Uncertainty Quantification and Experimental Design for Large-Scale Linear Inverse Problems under Gaussian Process Priors Cédric Travelletti D. Ginsbourger N. Linde 35 3 0 08 Sep 2021
Distributional Gradient Matching for Learning Uncertain Neural Dynamics Models Lenart Treven Philippe Wenk Florian Dorfler Andreas Krause OOD 11 2 0 22 Jun 2021
Efficient methods for Gaussian Markov random fields under sparse linear constraints David Bolin J. Wallin 20 4 0 03 Jun 2021
Hierarchical Non-Stationary Temporal Gaussian Processes With $L^1$ -Regularization Zheng Zhao Rui Gao Simo Särkkä 25 0 0 20 May 2021
Learning-enhanced robust controller synthesis with rigorous statistical and control-theoretic guarantees Christian Fiedler C. Scherer Sebastian Trimpe 24 15 0 07 May 2021
Practical and Rigorous Uncertainty Bounds for Gaussian Process Regression Christian Fiedler C. Scherer Sebastian Trimpe GP 31 65 0 06 May 2021
Bayesian Numerical Methods for Nonlinear Partial Differential Equations Junyang Wang Jon Cockayne O. Chkrebtii T. Sullivan Chris J. Oates 63 19 0 22 Apr 2021
3D Ensemble-Based Online Oceanic Flow Field Estimation for Underwater Glider Path Planning Felix H. Kong K. Y. C. To Gary G. Brassington Stuart Anstee Robert Fitch 12 1 0 09 Apr 2021
A Probabilistic State Space Model for Joint Inference from Differential Equations and Data Jonathan Schmidt Nicholas Kramer Philipp Hennig 15 23 0 18 Mar 2021
High-Dimensional Gaussian Process Inference with Derivatives Filip de Roos A. Gessner Philipp Hennig GP 17 16 0 15 Feb 2021
APIK: Active Physics-Informed Kriging Model with Partial Differential Equations Jialei Chen Zhehui Chen Chuck Zhang C. F. J. Wu 27 14 0 22 Dec 2020
Structured learning of rigid-body dynamics: A survey and unified view from a robotics perspective A. R. Geist Sebastian Trimpe AI4CE 24 17 0 11 Dec 2020
Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning A. Tompkins Rafael Oliveira F. Ramos 24 6 0 09 Oct 2020
Sequential Subspace Search for Functional Bayesian Optimization Incorporating Experimenter Intuition A. Shilton Sunil R. Gupta Santu Rana Svetha Venkatesh 19 3 0 08 Sep 2020
A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges L. Swiler Mamikon A. Gulian A. Frankel Cosmin Safta J. Jakeman GP AI4CE 8 103 0 16 Jun 2020
Learning Constrained Dynamics with Gauss Principle adhering Gaussian Processes A. R. Geist Sebastian Trimpe 25 21 0 23 Apr 2020
Gaussian Process Manifold Interpolation for Probabilistic Atrial Activation Maps and Uncertain Conduction Velocity Sam Coveney C. Corrado C. Roney D. O'Hare Steven E. Williams M. OÑeill Steven Niederer R. Clayton J. Oakley Richard D. Wilkinson 6 40 0 22 Apr 2020
Interpretable Safety Validation for Autonomous Vehicles Anthony Corso Mykel J. Kochenderfer 38 23 0 14 Apr 2020
Nonnegativity-Enforced Gaussian Process Regression Andrew Pensoneault Xiu Yang Xueyu Zhu 24 28 0 07 Apr 2020
Linearly Constrained Neural Networks J. Hendriks Carl Jidling A. Wills Thomas B. Schon 21 35 0 05 Feb 2020
Deep kernel learning for integral measurements Carl Jidling J. Hendriks Thomas B. Schon A. Wills 45 7 0 04 Sep 2019
Neutron Transmission Strain Tomography for Non-Constant Stress-Free Lattice Spacing J. Hendriks Carl Jidling Thomas B. Schon A. Wills C. Wensrich E. Kisi 12 6 0 15 May 2019
Gaussian processes with linear operator inequality constraints C. Agrell 10 38 0 10 Jan 2019
NIPS - Not Even Wrong? A Systematic Review of Empirically Complete Demonstrations of Algorithmic Effectiveness in the Machine Learning and Artificial Intelligence Literature Franz J. Király Bilal A. Mateen R. Sonabend 23 10 0 18 Dec 2018