From disorganized data to emergent dynamic models: Questionnaires to partial differential equations.

David W Sroczynski, Felix P Kemeth, Anastasia S Georgiou, Ronald R Coifman, Ioannis G Kevrekidis

Abstract read

In one paragraph

Article in PNAS nexus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

David W SroczynskiDepartment of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, USA.ORCID https://orcid.org/0000-0001-6077-2705

Felix P KemethDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.ORCID https://orcid.org/0000-0001-8535-8113

Anastasia S GeorgiouDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.ORCID https://orcid.org/0000-0001-9717-0599

Ronald R CoifmanDepartment of Mathematics, Yale University, New Haven, CT 06520, USA.ORCID https://orcid.org/0000-0001-7336-7784

Ioannis G KevrekidisDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.ORCID https://orcid.org/0000-0003-2220-3522

Funding

NIEHS NIH HHS 27307C0010

6 · The paper itself

Abstract

Starting with sets of disorganized observations of spatially varying and temporally evolving systems, obtained at different (also disorganized) sets of parameters, we demonstrate the data-driven derivation of parameter dependent, evolutionary partial differential equation (PDE) models capable of generating the data. This

Indexed as

generative modelslatent spacesmachine learningpartial differential equationsquestionnaires

Identifiers

PMID39898180

DOI10.1093/pnasnexus/pgaf018

PMCPMC11786195

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC

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 OpenQuestion graph.