ArticleComputer methods in applied mechanics and engineering2024
Improved multifidelity Monte Carlo estimators based on normalizing flows and dimensionality reduction techniques.
Article in Computer methods in applied mechanics and engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- On the performance of multi-fidelity and reduced-dimensional neural emulators for inference of physiological boundary conditions.Computers in biology and medicine · 2026Article
- Personalized and uncertainty-aware coronary hemodynamics simulations: From Bayesian estimation to improved multi-fidelity uncertainty quantification.Computer methods and programs in biomedicine · 2025Article
- Quantification of total uncertainty in the physics-informed reconstruction of CVSim-6 physiology.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- svZeroDSolver: A modular package for lumped-parameter cardiovascular simulations.Journal of open source software · 2025Article
- Neural Active Manifolds: Nonlinear Dimensionality Reduction for Uncertainty Quantification.Journal of scientific computing · 2025Article
- Article
- Quantification of total uncertainty in the physics-informed reconstruction of CVSim-6 physiology.ArXiv · 2024Article
- Personalized coronary and myocardial blood flow models incorporating CT perfusion imaging and synthetic vascular trees.Npj imaging · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
We study the problem of multifidelity uncertainty propagation for computationally expensive models. In particular, we consider the general setting where the high-fidelity and low-fidelity models have a dissimilar parameterization both in terms of number of random inputs and their probability distributions, which can be either known in closed form or provided through samples. We derive novel multifidelity Monte Carlo estimators which rely on a shared subspace between the high-fidelity and low-fidelity models where the parameters follow the same probability distribution, i.e., a standard Gaussian. We build the shared space employing normalizing flows to map different probability distributions into a common one, together with linear and nonlinear dimensionality reduction techniques, active subspaces and autoencoders, respectively, which capture the subspaces where the models vary the most. We then compose the existing low-fidelity model with these transformations and construct modified models with an increased correlation with the high-fidelity model, which therefore yield multifidelity estimators with reduced variance. A series of numerical experiments illustrate the properties and advantages of our approaches.
Indexed as
Identifiers
What OpenQuestion holds
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.