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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,051 papers · 148 categories

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8 results for “CoKriging”

Multifidelity Data Fusion via Gradient-Enhanced Gaussian Process Regressioncs.CE

Enhanced Gaussian process regression for multi-fidelity data fusion.

problem Combining data of varying fidelity levels for accurate predictions.

method Gradient-enhanced Cokriging method (GE-Cokriging) for QoI and its gradients.

result GE-Cokriging outperforms conventional multi-fidelity Cokriging in predicting QoI and gradients.

Gaussian process regression multi-fidelity data fusion gradient-enhanced Cokriging underdamped oscillator

2020-08-03abs ↗pdf ↗

Bivariate DeepKriging for Large-scale Spatial Interpolation of Wind Fieldsstat.ML

Proposes bivariate DeepKriging for efficient wind field prediction.

problem Challenges in predicting large-scale bivariate wind fields with high spatial variability and heterogeneity.

method Spatially dependent deep neural network (DNN) with embedding layer using spatial radial basis functions.

result Outperforms traditional cokriging predictors and reduces computation time.

wind fields spatial interpolation deep learning bivariate DeepKriging

2023-07-16abs ↗pdf ↗

When Bifidelity Meets CoKriging: An Efficient Physics-Informed Multifidelity Methodstat.ML

A new method combines multifidelity techniques to improve model accuracy with limited data.

problem Improving model accuracy with sparse accurate observations and stochastic simulation models.

method Combines bifidelity and CoKriging methods to estimate empirical statistics and construct a Gaussian process.

result Preserves linear physical constraints up to an error bound, leading to accurate model construction.

multifidelity physics-informed Gaussian process bifidelity CoKriging

2018-12-07abs ↗pdf ↗

Physics-Informed CoKriging: A Gaussian-Process-Regression-Based Multifidelity Method for Data-Model Convergencestat.ML

CoPhIK uses physics-informed Kriging to improve data-model convergence.

problem Improving data-model convergence in multifidelity problems.

method Physics-informed CoKriging (CoPhIK) combines PhIK and a parameterized GP to model discrepancies.

result CoPhIK reduces optimization cost and satisfies physical constraints up to an error bound.

Gaussian process regression multifidelity physics-informed CoKriging data-model convergence

2018-11-24abs ↗pdf ↗

On Cokriging, Neural Networks, and Spatial Blind Source Separation for Multivariate Spatial Predictioneess.SP

Spatial blind source separation simplifies multivariate spatial prediction.

problem Predicting multivariate measurements at unobserved locations with spatial dependencies.

method Spatial blind source separation as a pre-processing tool compared to Cokriging and neural networks.

result Spatial blind source separation simplifies spatial prediction by avoiding cross-dependencies.

spatial blind source separation multivariate spatial prediction spatial cross-dependencies Cokriging neural networks

2020-07-01abs ↗pdf ↗

SUM: Suboptimal Unitary Multi-task Learning Framework for Spatiotemporal Data Predictioncs.LG

SUM combines meta-learning with gradient descent to improve spatiotemporal data prediction.

problem Weak performance of traditional multi-task learning methods with few tasks.

method Two-step suboptimal unitary method (SUM) integrating meta-learning and gradient descent.

result SUM outperforms traditional methods on distant tasks and integrates with coKriging.

multi-task learning spatiotemporal data gradient descent meta-learning coKriging

2019-10-11abs ↗pdf ↗

Multifidelity Gaussian process regression for solving nonlinear partial differential equationsstat.ML

A new method uses multifidelity Gaussian process regression to solve nonlinear PDEs.

problem Efficiently solving nonlinear PDEs using kernel methods.

method Proposes a kernel learning approach based on cokriging for multifidelity simulations.

result Demonstrates improved performance on the Burgers' equation.

Gaussian process multifidelity kernel learning PDEs nonlinear equations

2026-05-11abs ↗pdf ↗

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Datastat.ML

This work improves surrogate models using low-fidelity data to enhance accuracy and efficiency.

problem Limited training data makes high-fidelity models unreliable.

method Uses low-fidelity data to augment input space and condition high-fidelity models.

result Increased predictive accuracy and reduced computational cost compared to existing methods.

multifidelity Gaussian process surrogate modeling scarce data low-fidelity

2026-03-23abs ↗pdf ↗