Evidence map›Paper›PMID 40078150›Full record

ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2025

Quantification of total uncertainty in the physics-informed reconstruction of CVSim-6 physiology.

Mario De Florio, Zongren Zou, Daniele E Schiavazzi, George Em Karniadakis

Abstract read
In one paragraph

Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. From PINNs to PIKANs: recent advances in physics-informed machine learning.Machine learning for computational science and engineering · 2025
    Article
  2. Quantification of total uncertainty in the physics-informed reconstruction of CVSim-6 physiology.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025
    Article
  3. Challenges and opportunities in uncertainty quantification for healthcare and biological systems.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025
    Review
  4. InferringInterface focus · 2024
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Mario De FlorioDivision of Applied Mathematics, Brown University, Providence, RI 02906, USA.
Zongren ZouDivision of Applied Mathematics, Brown University, Providence, RI 02906, USA.ORCID 0009-0000-0095-3539
Daniele E SchiavazziDepartment of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN 46556, USA.ORCID 0000-0001-9205-5989
George Em KarniadakisDivision of Applied Mathematics, Brown University, Providence, RI 02906, USA.ORCID 0000-0002-9713-7120

Funding

National Science FoundationNIH HHS
6 · The paper itself

Abstract

When predicting physical phenomena through simulation, quantification of the total uncertainty due to multiple sources is as crucial as making sure the underlying numerical model is accurate. Possible sources include irreducible

Indexed as

Models, BiologicalComputer SimulationHumansMonte Carlo MethodPhysicsUncertaintycardiovascular physiologyphysics-informed machine learningrandom-projection neural networkstime seriestotal uncertainty quantification

Identifiers

PMID40078150
PMCPMC12145504

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.