Evidence map›Paper›PMID 41852559›Full record

ArticleJournal of scientific computing2026

Fast Numerical Solvers for Parameter Identification Problems in Mathematical Biology.

Karolína Benková, John W Pearson, Mariya Ptashnyk

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of scientific computing, 2026. 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

3 authors.

Karolína BenkováThe Bayes Centre, The Maxwell Institute for Mathematical Sciences, 47 Potterrow, Edinburgh, EH8 9BT Scotland, UK.ORCID 0009-0009-8164-5989
John W PearsonSchool of Mathematics, The University of Edinburgh, The King's Buildings, Peter Guthrie Tait Road, Edinburgh, EH9 3FD Scotland, UK.ORCID https://orcid.org/0000-0002-6063-1766
Mariya PtashnykSchool of Mathematical and Computer Sciences, Heriot-Watt University, Riccarton Campus, Edinburgh, EH14 4AS Scotland, UK.ORCID https://orcid.org/0000-0003-4091-5080

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper, we consider effective discretization strategies and iterative solvers for nonlinear PDE-constrained optimization models of pattern evolution within biological processes. Upon a Sequential Quadratic Programming linearization of the optimization problem, we devise appropriate time-stepping schemes and discrete approximations of the cost functionals such that the discretization and optimization operations are commutative, a highly desirable property of a discretization of such problems. We formulate the large-scale, coupled linear systems in such a way that efficient preconditioned iterative methods can be applied within a Krylov subspace solver. Numerical experiments demonstrate the viability and efficiency of our approach.

Indexed as

Krylov subspace methodsParameter identificationPattern formationPDE-constrained optimizationPreconditioningTime-stepping

Identifiers

PMID41852559
PMCPMC12992359

What OpenQuestion holds

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Registered trials

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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.