Category: cs.GR
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Efficient Level-Crossing Probability Calculation for Gaussian Process Modeled Data
Efficient Level-Crossing Probability Calculation for Gaussian Process Modeled Data arXiv:2512.12442v1 Announce Type: new Abstract: Almost all scientific data have uncertainties originating from different sources. Gaussian process regression (GPR) models are a natural way to model data with Gaussian-distributed uncertainties. GPR also has the benefit of reducing I/O bandwidth and storage requirements for large scientific simulations.…
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An Incremental Non-Linear Manifold Approximation Method
An Incremental Non-Linear Manifold Approximation Method arXiv:2504.09068v1 Announce Type: new Abstract: Analyzing high-dimensional data presents challenges due to the “curse of dimensionality”, making computations intensive. Dimension reduction techniques, categorized as linear or non-linear, simplify such data. Non-linear methods are particularly essential for efficiently visualizing and processing complex data structures in interactive and graphical applications. This…
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ConfEviSurrogate: A Conformalized Evidential Surrogate Model for Uncertainty Quantification
ConfEviSurrogate: A Conformalized Evidential Surrogate Model for Uncertainty Quantification arXiv:2504.02919v1 Announce Type: new Abstract: Surrogate models, crucial for approximating complex simulation data across sciences, inherently carry uncertainties that range from simulation noise to model prediction errors. Without rigorous uncertainty quantification, predictions become unreliable and hence hinder analysis. While methods like Monte Carlo dropout and ensemble…