Evidence map›Paper›PMID 39063813›Full record

ReviewMaterials (Basel, Switzerland)2024

Advanced Computational Methods for Modeling, Prediction and Optimization-A Review.

Jaroslaw Krzywanski, Marcin Sosnowski, Karolina Grabowska, Anna Zylka, Lukasz Lasek, Agnieszka Kijo-Kleczkowska

Abstract readReview
In one paragraph

Review in Materials (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Tree-Based Methods to Predict Enzyme Inhibition.Methods in molecular biology (Clifton, N.J.) · 2026
    Article
  3. Review
  4. Article
  5. Surface Adhesion of Atrazine onto MgO/FeLangmuir : the ACS journal of surfaces and colloids · 2025
    Article
  6. Article
  7. Article
  8. Article
  9. Article
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

6 authors.

Jaroslaw KrzywanskiDepartment of Advanced Computational Methods, Faculty of Science and Technology, Jan Dlugosz University in Czestochowa, Armii Krajowej 13/15, 42-200 Czestochowa, Poland.ORCID 0000-0002-6364-7894
Marcin SosnowskiDepartment of Advanced Computational Methods, Faculty of Science and Technology, Jan Dlugosz University in Czestochowa, Armii Krajowej 13/15, 42-200 Czestochowa, Poland.ORCID 0000-0002-1906-9476
Karolina GrabowskaDepartment of Advanced Computational Methods, Faculty of Science and Technology, Jan Dlugosz University in Czestochowa, Armii Krajowej 13/15, 42-200 Czestochowa, Poland.ORCID 0000-0002-8323-8094
Anna ZylkaDepartment of Advanced Computational Methods, Faculty of Science and Technology, Jan Dlugosz University in Czestochowa, Armii Krajowej 13/15, 42-200 Czestochowa, Poland.ORCID 0000-0001-6241-0863
Lukasz LasekWladyslaw Bieganski Collegium Medicum, Jan Dlugosz University in Czestochowa, Armii Krajowej 13/15, 42-200 Czestochowa, Poland.ORCID 0009-0001-1211-0624
Agnieszka Kijo-KleczkowskaDepartment of Thermal Machinery, Faculty of Mechanical Engineering and Computer Science, Czestochowa University of Technology, 42-201 Czestochowa, Poland.

Funding

Czestochowa University of Technology, Poland BS/PB-1-100-301/2024/PNational Science Center, Poland 2018/29/B/ST8/ 00442National Science Centre, Poland 2023/07/X/ST8/01229
6 · The paper itself

Abstract

This paper provides a comprehensive review of recent advancements in computational methods for modeling, simulation, and optimization of complex systems in materials engineering, mechanical engineering, and energy systems. We identified key trends and highlighted the integration of artificial intelligence (AI) with traditional computational methods. Some of the cited works were previously published within the topic: "Computational Methods: Modeling, Simulations, and Optimization of Complex Systems"; thus, this article compiles the latest reports from this field. The work presents various contemporary applications of advanced computational algorithms, including AI methods. It also introduces proposals for novel strategies in materials production and optimization methods within the energy systems domain. It is essential to optimize the properties of materials used in energy. Our findings demonstrate significant improvements in accuracy and efficiency, offering valuable insights for researchers and practitioners. This review contributes to the field by synthesizing state-of-the-art developments and suggesting directions for future research, underscoring the critical role of these methods in advancing engineering and technological solutions.

Indexed as

artificial intelligencecomplex systemsenergy systemsmachine learningmaterial engineeringmodelingoptimizationsimulation

Identifiers

PMID39063813
PMCPMC11279266

What OpenQuestion 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 OpenQuestion graph.