Cover Image

Dimensionality reduction can be used as a surrogate model for high-dimensional forward uncertainty quantification

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Kim, Jungho, Yi, Sang-ri, Wang, Ziqi
Format:	Preprint
Published:	2024
Subjects:	Applications Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A composition of simplified physics-based model with neural operator for trajectory-level seismic response predictions of structural systems
by: Kim, Jungho, et al.
Published: (2025)

Uncertainty quantification for seismic response using dimensionality reduction-based stochastic simulator
by: Kim, Jungho, et al.
Published: (2024)

Long-range Ising model for regional-scale seismic risk analysis
by: Oh, Sebin, et al.
Published: (2024)

Surrogate modeling for uncertainty quantification in nonlinear dynamics
by: Marelli, S., et al.
Published: (2025)

Adaptive active subspace-based metamodeling for high-dimensional reliability analysis
by: Kim, Jungho, et al.
Published: (2023)

Partially stochastic deep learning with uncertainty quantification for model predictive heating control
by: Hannula, Emma, et al.
Published: (2025)

Constructing balanced datasets for predicting failure modes in structural systems under seismic hazards
by: Kim, Jungho, et al.
Published: (2025)

Accelerated System-Reliability-based Disaster Resilience Analysis for Structural Systems
by: Kim, Taeyong, et al.
Published: (2024)

A physics and data co-driven surrogate modeling method for high-dimensional rare event simulation
by: Xian, Jianhua, et al.
Published: (2023)

Isotonic Quantile Regression Averaging for uncertainty quantification of electricity price forecasts
by: Lipiecki, Arkadiusz, et al.
Published: (2025)

Toward using explainable data-driven surrogate models for treating performance-based seismic design as an inverse engineering problem
by: Esteghamati, Mohsen Zaker
Published: (2025)

Local transfer learning Gaussian process modeling, with applications to surrogate modeling of expensive computer simulators
by: Wang, Xinming, et al.
Published: (2024)

Stratospheric aerosol source inversion: Noise, variability, and uncertainty quantification
by: Hart, J., et al.
Published: (2024)

Latent mixed-effect models for high-dimensional longitudinal data
by: Ong, Priscilla, et al.
Published: (2024)

mNARX+: A surrogate model for complex dynamical systems using manifold-NARX and automatic feature selection
by: Schär, S., et al.
Published: (2025)

Uncertainty quantification in automated valuation models with spatially weighted conformal prediction
by: Hjort, Anders, et al.
Published: (2023)

Sequential design of multi-fidelity computer experiments: maximizing the rate of stepwise uncertainty reduction
by: Stroh, Rémi, et al.
Published: (2020)

Variational Bayesian surrogate modelling with application to robust design optimisation
by: Archbold, Thomas A., et al.
Published: (2024)

Rashomon perspective for measuring uncertainty in the survival predictive maintenance models
by: Yardimci, Yigitcan, et al.
Published: (2025)

Uncertainty quantification for seismic response using dimensionality reduction‐based stochastic simulator
by: Jungho Kim, et al.
Published: (2024)

Confidence interval estimation of mixed oil length with conditional diffusion model
by: Yang, Yanfeng, et al.
Published: (2024)

Uncertainty quantification for data-driven weather models
by: Bülte, Christopher, et al.
Published: (2024)

Quantifying uncertainty in climate projections with conformal ensembles
by: Harris, Trevor, et al.
Published: (2024)

Feature Augmentations for High-Dimensional Learning
by: Zhu, Xiaonan, et al.
Published: (2025)

Calibrating dimension reduction hyperparameters in the presence of noise
by: Lin, Justin, et al.
Published: (2023)

Filtered Partial Differential Equations: a robust surrogate constraint in physics-informed deep learning framework
by: Zhang, Dashan, et al.
Published: (2023)

Anomaly Detection in Power Grids via Context-Agnostic Learning
by: Park, SangWoo, et al.
Published: (2024)

Measurement Uncertainty: Relating the uncertainties of physical and virtual measurements
by: Cramer, Simon, et al.
Published: (2024)

FCPCA: Fuzzy clustering of high-dimensional time series based on common principal component analysis
by: Ma, Ziling, et al.
Published: (2025)

Active learning for structural reliability analysis with multiple limit state functions through variance-enhanced PC-Kriging surrogate models
by: A., J. Moran, et al.
Published: (2023)

Score-based diffusion models for diffuse optical tomography with uncertainty quantification
by: Schneider, Fabian, et al.
Published: (2026)

Discovering deposition process regimes: leveraging unsupervised learning for process insights, surrogate modeling, and sensitivity analysis
by: Suntaxi, Geremy Loachamín, et al.
Published: (2024)

Recorded Versus Synthetic Spectral-compatible Ground Motions: A Comparative Analysis of Structural Seismic Responses
by: Kim, Jungho, et al.
Published: (2025)

Nonlinear Dimensionality Reduction with Diffusion Maps in Practice
by: Beier, Sönke, et al.
Published: (2026)

Classification problem in liability insurance using machine learning models: a comparative study
by: Qazvini, Marjan
Published: (2024)

Pandemic model with data-driven phase detection, a study using COVID-19 data
by: Liu, Yuansan, et al.
Published: (2021)

DRtool: An Interactive Tool for Analyzing High-Dimensional Clusterings
by: Lin, Justin, et al.
Published: (2025)

Design a Metric Robust to Complicated High Dimensional Noise for Efficient Manifold Denoising
by: Wu, Hau-Tieng
Published: (2024)

Effective climate policies for major emission reductions of ozone precursors: Global evidence from two decades
by: Yao, Ningning, et al.
Published: (2025)

An unsupervised learning approach to evaluate questionnaire data -- what one can learn from violations of measurement invariance
by: Hahn-Klimroth, Max, et al.
Published: (2023)