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Found 10 papers, 0 papers with code
Gaussian Process Regression with Soft Inequality and Monotonicity Constraints
Introducing the QHMC method to the inequality and monotonicity constrained GP regression in the probabilistic sense, our approach improves the accuracy and reduces the variance in the resulting GP model.
Solving Seismic Wave Equations on Variable Velocity Models with Fourier Neural Operator
Previous works that concentrate on solving the wave equation by neural networks consider either a single velocity model or multiple simple velocity models, which is restricted in practice.
Graphical Gaussian Process Regression Model for Aqueous Solvation Free Energy Prediction of Organic Molecules in Redox Flow Battery
The solvation free energy of organic molecules is a critical parameter in determining emergent properties such as solubility, liquid-phase equilibrium constants, and pKa and redox potentials in an organic redox flow battery.
A Physics-Informed Neural Network Framework For Partial Differential Equations on 3D Surfaces: Time-Dependent Problems
In this paper, we show a physics-informed neural network solver for the time-dependent surface PDEs.
Augmented Gaussian Random Field: Theory and Computation
We prove that under certain conditions, the observable and its derivatives of any order are governed by a single Gaussian random field, which is the aforementioned AGRF.
Statistics Theory Probability Statistics Theory
Multifidelity Data Fusion via Gradient-Enhanced Gaussian Process Regression
We compare this method with the conventional multi-fidelity Cokriging method that does not use gradients information, and the result suggests that GE-Cokriging has a better performance in predicting both QoI and its gradients.
Nonnegativity-Enforced Gaussian Process Regression
Gaussian Process (GP) regression is a flexible non-parametric approach to approximate complex models.
When Bifidelity Meets CoKriging: An Efficient Physics-Informed Multifidelity Method
In this work, we propose a framework that combines the approximation-theory-based multifidelity method and Gaussian-process-regression-based multifidelity method to achieve data-model convergence when stochastic simulation models and sparse accurate observation data are available.
Physics-Informed CoKriging: A Gaussian-Process-Regression-Based Multifidelity Method for Data-Model Convergence
In this work, we propose a new Gaussian process regression (GPR)-based multifidelity method: physics-informed CoKriging (CoPhIK).
Physics-Information-Aided Kriging: Constructing Covariance Functions using Stochastic Simulation Models
We also provide an error estimate in preserving the physical constraints when errors are included in the stochastic model realizations.
