ReviewData science and engineering2022
Dimensionality Reduction in Surrogate Modeling: A Review of Combined Methods.
Review in Data science and engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
12 citing papers in PubMed.
- Uncertainty-aware temperature field reconstruction from sparse samples and sensing via latent-space enhancement for GIS digital twin applications.Scientific reports · 2026Article
- Unsupervised Characterization of Temporal Dataset Shifts as an Early Indicator of AI Performance Variations: Evaluation Study Using the Medical Information Mart for Intensive Care-IV Dataset.JMIR medical informatics · 2025Article
- Data-driven phenotypic profiling of prediabetes reveals heterogeneous cardiometabolic risks in Chinese adults.Cardiovascular diabetology · 2025Article
- Multivariate Patterns in Mental Health Burden and Psychiatric Resource Allocation in Europe: A Principal Component Analysis.Healthcare (Basel, Switzerland) · 2025Article
- Exploring unsupervised feature extraction algorithms: tackling high dimensionality in small datasets.Scientific reports · 2025Article
- Enhancing Reliability in Redundant Homogeneous Sensor Arrays with Self-X and Multidimensional Mapping.Sensors (Basel, Switzerland) · 2025Article
- Mathematical Methods Applied to the Problem of Dairy Cow Replacements: A Scoping Review.Animals : an open access journal from MDPI · 2025Review
- Deep Clustering-Based Immunotherapy Prediction for Gastric Cancer mRNA Vaccine Development.International journal of molecular sciences · 2025Article
- Best Practices in Developing a Workflow for Uncertainty Quantification for Modeling the Biodegradation of Mg-Based Implants.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- CuPCA: a web server for pan-cancer association analysis of large-scale cuproptosis-related genes.Database : the journal of biological databases and curation · 2024Article
- Characteristic Changes of the Stance-Phase Plantar Pressure Curve When Walking Uphill and Downhill: Cross-Sectional Study.Journal of medical Internet research · 2024Article
- Ensemble Learning, Deep Learning-Based and Molecular Descriptor-Based Quantitative Structure-Activity Relationships.Molecules (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Surrogate modeling has been popularized as an alternative to full-scale models in complex engineering processes such as manufacturing and computer-assisted engineering. The modeling demand exponentially increases with complexity and number of system parameters, which consequently requires higher-dimensional engineering solving techniques. This is known as the curse of dimensionality. Surrogate models are commonly used to replace costly computational simulations and modeling of complex geometries. However, an ongoing challenge is to reduce execution and memory consumption of high-complexity processes, which often exhibit nonlinear phenomena. Dimensionality reduction algorithms have been employed for feature extraction, selection, and elimination for simplifying surrogate models of high-dimensional problems. By applying dimensionality reduction to surrogate models, less computation is required to generate surrogate model parts while retaining sufficient representation accuracy of the full process. This paper aims to review the current literature on dimensionality reduction integrated with surrogate modeling methods. A review of the current state-of-the-art dimensionality reduction and surrogate modeling methods is introduced with a discussion of their mathematical implications, applications, and limitations. Finally, current studies that combine the two topics are discussed and avenues of further research are presented.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.