Spatial metamodels improve root finding in uncertain oracle responses.
problem Finding roots in noisy, location-dependent oracle responses.
method Propose spatial metamodels to infer oracle distribution and update Bayesian knowledge.
result Spatial PBA algorithm outperforms earlier models in synthetic and real-world problems.
MetaStackVis aids in choosing better metamodels for stacking ensembles.
problem Difficulty in selecting optimal metamodels for stacking ensembles.
method Interactive visualization tool to explore and compare different metamodels.
result Alternative metamodels significantly improve stacking ensemble performance.
New deep learning model optimizes energy use in buildings.
problem Optimizing energy use and comfort in large buildings.
method Transformer-based metamodel trained with simulation and sensor data, calibrated with CMA-ES, optimized with multi-objective algorithms.
result Optimal settings reduce energy loads while maintaining thermal comfort and air quality.
New framework optimizes complex systems decisions via simulation.
problem Optimizing strategic, tactical, and operational decisions in complex systems.
method Global-local metamodel assisted two-stage optimization via simulation.
result Framework efficiently searches for optimal decisions with unknown objective.
Review of quantile regression methods for stochastic computer experiments.
problem Quantile regression in stochastic computer experiments.
method Six metamodels categorized by order statistics, functional approaches, and Bayesian methods tested on various problems.
result Metamodels reveal good contrasts, providing guidelines for selecting the best method.
Study evaluates GP metamodels and sequential designs for noisy level set estimation.
problem Efficiently reconstructing the level set of a noisy function.
method Investigates Gaussian process (GP) and Student-t process (TP) metamodels, along with various acquisition functions.
result GPs with Student-t observations and TPs perform better than classification GPs in noisy conditions.
Surrogate models improve tidal model calibration efficiency.
problem Efficiently calibrate complex tidal models for climate change scenarios.
method Proposes two surrogate-based methods to replace complex models: PODEn3DVAR and POD-PCE-3DVAR.
result Both methods show superior convergence and robustness to noise compared to classical 3DVAR.
This thesis explores adaptive sampling techniques for creating efficient surrogate models.
problem High computational cost of numerical simulations.
method Adaptive sampling techniques for Kriging metamodels.
result Comprehensive comparison of adaptive sampling techniques for Kriging.
Bayesian optimization uses shared latent variables for multiple systems.
problem Optimizing systems with limited data and unknown relationships.
method Shared latent variables, Bayesian inference, probabilistic metamodel.
result Performance improvement in zero-, one-, and few-shot settings.
Paper develops efficient Bayesian inference for enzymatic SRNs with LNA metamodel.
problem Bayesian inference for nonlinear SDE-based mechanistic models with partial observations and measurement errors.
method Interpretable Bayesian updating LNA metamodel and efficient posterior sampling.
result Proposed approach demonstrates promising performance in empirical studies.
E-QRGMM accelerates uncertainty quantification in simulations.
problem Challenges in covariate-dependent uncertainty quantification.
method Integrates cubic Hermite interpolation with gradient estimation.
result Substantially improves computational efficiency and accuracy.
Study compares neural nets and gradient boosting for traffic optimization, revealing accuracy issues near local optima.
problem Accuracy of neural nets and gradient boosting models in traffic optimization near local optima.
method 16 neural nets and 20 genetic algorithm settings analyzed for traffic optimization.
result Accuracy drops near local optima, affecting traffic optimization efficiency.
A new method uses Gaussian processes to efficiently model and compute counterparty credit valuation adjustments (CVA).
problem Efficiently modeling and computing CVA for large OTC derivative portfolios.
method Multi-Gaussian process regression approach to learn a metamodel for the mark-to-market cube of a derivative portfolio.
result The method accurately and efficiently computes CVA for interest rate swap portfolios.
Adaptive batching improves Gaussian process surrogates for noisy level set estimation.
problem Learning the level set of noisy simulator responses.
method Developed four novel adaptive batching schemes for Gaussian process metamodels.
result Adaptive batching brings significant computational speed-ups with minimal loss of modeling fidelity.
A new method predicts oil movement in reservoirs using deep learning.
problem Assessing dynamics of multiphase fluid flow in oil reservoirs.
method Metamodel based on Variational Autoencoder and Recurrent Neural Network.
result The Metamodel accurately predicts flow rates, pressure, and fluid saturations.
BayCANN uses ANN to speed up Bayesian calibration in health sciences.
problem Bayesian calibration's practical and computational burdens in health decision sciences.
method BayCANN trains an ANN metamodel to calibrate parameters probabilistically, comparing accuracy and speed to direct Bayesian calibration.
result BayCANN is more accurate and faster than direct Bayesian calibration methods.
A new framework interprets machine learning models using Gaussian processes.
problem Interpreting complex machine learning models.
method Gaussian process metamodeling to capture response surfaces.
result Maximizing likelihood function for variable importance.
Polynomial chaos expansion improves machine learning regression accuracy.
problem Improving pointwise prediction accuracy in machine learning regression.
method Data-driven polynomial chaos expansion trained on input-output data.
result PCE metamodels achieve comparable accuracy to ML models on benchmark datasets.
Paper proposes an efficient AL-GP method for CDF/CCDF estimation in UQ.
problem Estimating full probability distribution in forward UQ analysis.
method Active learning-based Gaussian process (AL-GP) metamodelling method.
result Efficient estimation of CDF/CCDF without explicit discretization.
New method improves stochastic kriging for high-dimensional simulations.
problem High-dimensional simulation models require prohibitive sample sizes and computational costs.
method Tensor Markov kernels and sparse grid experimental designs.
result Sample complexity grows only slightly with dimensionality, improving accuracy and efficiency.
The aim of the present paper is to develop a strategy for solving reliability-based design optimization (RBDO) problems that remains applicable when the performance models are expensive to evaluate. Starting with the premise that simulation-based approaches are not affordable for such problems, and that the most-probab…
LMGPs extend GPs to handle mixed data, offering better accuracy and interpretability.
problem Handling mixed data types (quantitative and qualitative) in metamodeling.
method Introduce LMGPs that learn a latent manifold for qualitative inputs, using a low-rank linear map.
result LMGPs outperform existing methods in accuracy and versatility.
We investigate two new strategies for the numerical solution of optimal stopping problems within the Regression Monte Carlo (RMC) framework of Longstaff and Schwartz. First, we propose the use of stochastic kriging (Gaussian process) meta-models for fitting the continuation value. Kriging offers a flexible, nonparametr…
In the field of structural reliability, the Monte-Carlo estimator is considered as the reference probability estimator. However, it is still untractable for real engineering cases since it requires a high number of runs of the model. In order to reduce the number of computer experiments, many other approaches known as …
Deep learning models optimize urban transportation scheduling.
problem Optimizing transportation systems with complex dynamics and large data sets.
method Developed deep learning metamodels for simulators and reinforcement learning algorithms.
result Improved optimal scheduling of travelers on transportation networks.
We develop a framework for warm-starting Bayesian optimization, that reduces the solution time required to solve an optimization problem that is one in a sequence of related problems. This is useful when optimizing the output of a stochastic simulator that fails to provide derivative information, for which Bayesian opt…
Spatial variable selection is crucial for reliable spatial predictions in machine learning.
problem Spatial autocorrelation leads to overfitting and poor spatial predictions.
method Used Random Forests with non-spatial and spatial cross-validation strategies.
result Spatial variable selection is essential for reliable spatial predictions.
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.
The paper introduces groupoid racks for spatial surfaces.
problem Coloring diagrams of spatial surfaces for invariant calculation.
method Introduces groupoid racks with universal properties.
result Groupoid racks provide an invariant for spatial surfaces.
STICC clusters geographic objects considering both spatial contiguity and attributes.
problem Discovering repeated geographic patterns with spatial contiguity.
method Spatial Toeplitz Inverse Covariance-Based Clustering (STICC) method.
result STICC significantly outperforms baseline methods in adjusted rand index and macro-F1 score.
Spatial understanding is a fundamental problem with wide-reaching real-world applications. The representation of spatial knowledge is often modeled with spatial templates, i.e., regions of acceptability of two objects under an explicit spatial relationship (e.g., "on", "below", etc.). In contrast with prior work that r…
GCNs model complex spatial patterns of POI check-ins.
problem Capturing complex spatial patterns in irregular data.
method Graph Convolutional Neural Networks (GCNs) for semi-supervised prediction.
result Demonstrates feasibility of GCNs for complex geographic data.
Defines non-parabolic curves in spatial hybrid space with applications.
problem Defining and analyzing non-parabolic spatial hybrid framed curves.
method Definition and proof of existence and uniqueness theorem for non-parabolic spatial hybrid framed curves.
result Existence and uniqueness theorem for non-parabolic spatial hybrid framed curves.
A framework converts spatial data into embeddings for insurance risk modelling.
problem Improving underwriting precision and risk management in insurance with spatial data.
method Multi-view contrastive learning framework for generating spatial embeddings.
result Spatial embeddings consistently improve predictive accuracy across various models.
This study analyses, through cross-section estimation methods, the influence of spatial effects in the conditional product convergence in the parishes' economies of mainland Portugal between 1991 and 2001 (the last year with data available for this spatial disaggregation level). To analyse the data, Moran's I statistic…
Proposes a self-supervised method for generating spatial audio from monaural audio and video.
problem Generating spatial audio from monaural audio and video recordings is challenging and expensive.
method Uses a self-supervised network with an auxiliary classifier to classify video channels and generate spatial audio.
result The proposed method effectively generates spatial audio from monaural audio and video.
This paper reviews spatial and spatiotemporal volatility models.
problem Capturing spatial dependence in volatility of spatial and spatiotemporal data.
method Review of time series volatility models and their extensions.
result Comparison and practical recommendations for spatial and spatiotemporal volatility models.
Spatially-aware machine learning predicts gentrification better than non-spatial models.
problem Predicting gentrification in real estate sales.
method Combining data science, machine learning, and spatial analysis techniques.
result Spatially-conscious machine learning models outperform non-spatial models.
New method estimates spatial weights matrix for lattice data, improving prediction accuracy.
problem Estimating spatial dependence structure for regular lattice data.
method Adaptive lasso with cross-sectional resampling to estimate sparse spatial weights matrix.
result Improves prediction accuracy of nitrogen dioxide concentrations.
Spatial graphs can be unknotted with region crossing changes.
problem Unknotted spatial graphs composed of theta-curves.
method Region crossing changes on regions of theta-curves.
result Spatial graphs of theta-curves can be unknotted.
NCS enables efficient and accurate conditional simulation for complex spatial processes.
problem Challenges in simulating from complex spatial process distributions.
method Neural diffusion models and conditional score-based diffusion.
result NCS outperforms traditional methods in efficiency and accuracy.
Spatial Adapter adds structured spatial representation to frozen predictors.
problem Efficiently adding spatial structure to pre-trained models.
method Structured spatial decomposition and closed-form covariance for residual fields.
result Adapter improves spatial prediction and uncertainty quantification.
Grid homology theory for spatial graphs extends skein sequence.
problem No specific problem stated; focuses on extending a sequence.
method Defined grid homology theory for spatial graphs and extended skein sequence.
result Skein exact sequence extended to grid homology for spatial graphs.
A new adaptive kriging method improves binary classification of mechanical problems.
problem Efficient binary classification of mechanical problems with high fluctuation.
method Monte Carlo-intersite Voronoi (MiVor) adaptive scheme for regression surrogate model.
result The MiVor algorithm provides accurate binary classification with fewer observation points for highly fluctuating response surfaces.
New invariants distinguish spatial graphs not previously possible.
problem Distinguishing spatial graphs using Dehn colorings.
method Developed vertex-weight invariants based on Dehn colorings.
result Found spatial graphs distinguishable by vertex-weight invariants.
Investigates transfer learning in spatial statistics.
problem Applying transfer learning to spatial statistics.
method Simple MLP models for spatial data.
result Potential of transfer learning in spatial statistics.
Bayesian spatial predictive synthesis improves spatial data predictions.
problem Model misspecification and heterogeneity in spatial data.
method Bayesian ensemble methodology capturing spatially-varying model uncertainty and performance heterogeneity.
result Synthesized predictions outperform standard methods in accuracy and uncertainty quantification.
Spatially constrained Gaussian mixture models reduce covariance complexity.
problem High dimensionality in finite mixture models for spatial data.
method Spatial covariance constraint with only four free parameters.
result Improves clustering of multi-way spatial data and inference of spatial patterns.