Physics informed neural network with multiple subnetworks for predicting rheological behavior of cementitious materials Article Swipe
Tianjie Zhang
,
Donglei Wang
,
Yang Lu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/s42452-025-07786-5
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/s42452-025-07786-5
Traditional Physics-Informed Neural Networks (PINNs) utilize a fully-connected architecture, causing shared parameters among outputs and subsequent accuracy degradation. We proposed a MultiSubPINN model to address this by deploying independent networks for distinct predictions, minimizing output interference and preserving individual output optimizations. Our results demonstrate that MultiSubPINN significantly outperforms conventional PINN models in solving complex partial differential equations (PDEs). Notably, MultiSubPINN achieved a reduction in Mean Square Error (MSE) by 32.7%, 9.9%, and 52.8% for shear stress, viscosity, and structure parameter predictions, respectively. Moreover, we introduce a novel physics-based sampling strategy that leverages residual distribution and physics principles to further refine MultiSubPINN’s performance. This approach markedly surpasses random sampling strategies, reducing MSE by approximately 97.7%, 90.2%, and 38.3% for the aforementioned parameters. Importantly, the enhancements in prediction accuracy are achieved with a marginal increase (0.1%) in training time, underscoring MultiSubPINN’s efficacy and potential for broader application in material simulation domains.
Related Topics
Concepts
No concepts available.
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s42452-025-07786-5
- https://link.springer.com/content/pdf/10.1007/s42452-025-07786-5.pdf
- OA Status
- diamond
- References
- 37
- OpenAlex ID
- https://openalex.org/W4415381407
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4415381407Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1007/s42452-025-07786-5Digital Object Identifier
- Title
-
Physics informed neural network with multiple subnetworks for predicting rheological behavior of cementitious materialsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-10-21Full publication date if available
- Authors
-
Tianjie Zhang, Donglei Wang, Yang LuList of authors in order
- Landing page
-
https://doi.org/10.1007/s42452-025-07786-5Publisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007/s42452-025-07786-5.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://link.springer.com/content/pdf/10.1007/s42452-025-07786-5.pdfDirect OA link when available
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
37Number of works referenced by this work
Full payload
| id | https://openalex.org/W4415381407 |
|---|---|
| doi | https://doi.org/10.1007/s42452-025-07786-5 |
| ids.doi | https://doi.org/10.1007/s42452-025-07786-5 |
| ids.openalex | https://openalex.org/W4415381407 |
| fwci | 0.0 |
| type | article |
| title | Physics informed neural network with multiple subnetworks for predicting rheological behavior of cementitious materials |
| biblio.issue | 11 |
| biblio.volume | 7 |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T12293 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9940999746322632 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2205 |
| topics[0].subfield.display_name | Civil and Structural Engineering |
| topics[0].display_name | Dam Engineering and Safety |
| topics[1].id | https://openalex.org/T10892 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9915000200271606 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2212 |
| topics[1].subfield.display_name | Ocean Engineering |
| topics[1].display_name | Drilling and Well Engineering |
| topics[2].id | https://openalex.org/T12190 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9908999800682068 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2215 |
| topics[2].subfield.display_name | Building and Construction |
| topics[2].display_name | Innovations in Concrete and Construction Materials |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| language | en |
| locations[0].id | doi:10.1007/s42452-025-07786-5 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S5407042868 |
| locations[0].source.issn | 3004-9261 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 3004-9261 |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Discover Applied Sciences |
| locations[0].source.host_organization | |
| locations[0].source.host_organization_name | |
| locations[0].license | cc-by-nc-nd |
| locations[0].pdf_url | https://link.springer.com/content/pdf/10.1007/s42452-025-07786-5.pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by-nc-nd |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Discover Applied Sciences |
| locations[0].landing_page_url | https://doi.org/10.1007/s42452-025-07786-5 |
| locations[1].id | pmh:oai:doaj.org/article:99cff7dc4ca14dada859b80bfc0a951b |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306401280 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | Discover Applied Sciences, Vol 7, Iss 11, Pp 1-23 (2025) |
| locations[1].landing_page_url | https://doaj.org/article/99cff7dc4ca14dada859b80bfc0a951b |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5003966240 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-6697-0777 |
| authorships[0].author.display_name | Tianjie Zhang |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Tianjie Zhang |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5038300996 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-5888-7439 |
| authorships[1].author.display_name | Donglei Wang |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Donglei Wang |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5068651974 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-2330-4237 |
| authorships[2].author.display_name | Yang Lu |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Yang Lu |
| authorships[2].is_corresponding | False |
| has_content.pdf | True |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://link.springer.com/content/pdf/10.1007/s42452-025-07786-5.pdf |
| open_access.oa_status | diamond |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-22T00:00:00 |
| display_name | Physics informed neural network with multiple subnetworks for predicting rheological behavior of cementitious materials |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T12293 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9940999746322632 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2205 |
| primary_topic.subfield.display_name | Civil and Structural Engineering |
| primary_topic.display_name | Dam Engineering and Safety |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | doi:10.1007/s42452-025-07786-5 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S5407042868 |
| best_oa_location.source.issn | 3004-9261 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 3004-9261 |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Discover Applied Sciences |
| best_oa_location.source.host_organization | |
| best_oa_location.source.host_organization_name | |
| best_oa_location.license | cc-by-nc-nd |
| best_oa_location.pdf_url | https://link.springer.com/content/pdf/10.1007/s42452-025-07786-5.pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Discover Applied Sciences |
| best_oa_location.landing_page_url | https://doi.org/10.1007/s42452-025-07786-5 |
| primary_location.id | doi:10.1007/s42452-025-07786-5 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S5407042868 |
| primary_location.source.issn | 3004-9261 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 3004-9261 |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Discover Applied Sciences |
| primary_location.source.host_organization | |
| primary_location.source.host_organization_name | |
| primary_location.license | cc-by-nc-nd |
| primary_location.pdf_url | https://link.springer.com/content/pdf/10.1007/s42452-025-07786-5.pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Discover Applied Sciences |
| primary_location.landing_page_url | https://doi.org/10.1007/s42452-025-07786-5 |
| publication_date | 2025-10-21 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W2159254771, https://openalex.org/W4229562219, https://openalex.org/W2950093768, https://openalex.org/W2907375340, https://openalex.org/W2801832296, https://openalex.org/W2900899881, https://openalex.org/W2965592990, https://openalex.org/W2011565685, https://openalex.org/W2079579652, https://openalex.org/W3176997027, https://openalex.org/W2090221573, https://openalex.org/W2914202198, https://openalex.org/W1993175125, https://openalex.org/W2009448684, https://openalex.org/W4406104893, https://openalex.org/W4391798023, https://openalex.org/W4319996377, https://openalex.org/W3014009018, https://openalex.org/W3163993681, https://openalex.org/W4407183917, https://openalex.org/W4395683262, https://openalex.org/W4383213217, https://openalex.org/W4220687847, https://openalex.org/W3209909540, https://openalex.org/W3153200540, https://openalex.org/W4220717841, https://openalex.org/W4213199992, https://openalex.org/W3003922491, https://openalex.org/W3010839048, https://openalex.org/W4406601034, https://openalex.org/W4362579463, https://openalex.org/W3185095713, https://openalex.org/W4366245720, https://openalex.org/W3154575637, https://openalex.org/W4241901443, https://openalex.org/W2075562579, https://openalex.org/W2969919798 |
| referenced_works_count | 37 |
| abstract_inverted_index.a | 7, 21, 62, 86, 131 |
| abstract_inverted_index.We | 19 |
| abstract_inverted_index.by | 27, 69, 112 |
| abstract_inverted_index.in | 52, 64, 125, 135, 146 |
| abstract_inverted_index.to | 24, 98 |
| abstract_inverted_index.we | 84 |
| abstract_inverted_index.MSE | 111 |
| abstract_inverted_index.Our | 42 |
| abstract_inverted_index.and | 15, 37, 72, 78, 95, 116, 141 |
| abstract_inverted_index.are | 128 |
| abstract_inverted_index.for | 31, 74, 118, 143 |
| abstract_inverted_index.the | 119, 123 |
| abstract_inverted_index.Mean | 65 |
| abstract_inverted_index.PINN | 50 |
| abstract_inverted_index.This | 103 |
| abstract_inverted_index.that | 45, 91 |
| abstract_inverted_index.this | 26 |
| abstract_inverted_index.with | 130 |
| abstract_inverted_index.(MSE) | 68 |
| abstract_inverted_index.38.3% | 117 |
| abstract_inverted_index.52.8% | 73 |
| abstract_inverted_index.9.9%, | 71 |
| abstract_inverted_index.Error | 67 |
| abstract_inverted_index.among | 13 |
| abstract_inverted_index.model | 23 |
| abstract_inverted_index.novel | 87 |
| abstract_inverted_index.shear | 75 |
| abstract_inverted_index.time, | 137 |
| abstract_inverted_index.(0.1%) | 134 |
| abstract_inverted_index.32.7%, | 70 |
| abstract_inverted_index.90.2%, | 115 |
| abstract_inverted_index.97.7%, | 114 |
| abstract_inverted_index.Neural | 3 |
| abstract_inverted_index.Square | 66 |
| abstract_inverted_index.models | 51 |
| abstract_inverted_index.output | 35, 40 |
| abstract_inverted_index.random | 107 |
| abstract_inverted_index.refine | 100 |
| abstract_inverted_index.shared | 11 |
| abstract_inverted_index.(PDEs). | 58 |
| abstract_inverted_index.(PINNs) | 5 |
| abstract_inverted_index.address | 25 |
| abstract_inverted_index.broader | 144 |
| abstract_inverted_index.causing | 10 |
| abstract_inverted_index.complex | 54 |
| abstract_inverted_index.further | 99 |
| abstract_inverted_index.outputs | 14 |
| abstract_inverted_index.partial | 55 |
| abstract_inverted_index.physics | 96 |
| abstract_inverted_index.results | 43 |
| abstract_inverted_index.solving | 53 |
| abstract_inverted_index.stress, | 76 |
| abstract_inverted_index.utilize | 6 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Networks | 4 |
| abstract_inverted_index.Notably, | 59 |
| abstract_inverted_index.accuracy | 17, 127 |
| abstract_inverted_index.achieved | 61, 129 |
| abstract_inverted_index.approach | 104 |
| abstract_inverted_index.distinct | 32 |
| abstract_inverted_index.domains. | 149 |
| abstract_inverted_index.efficacy | 140 |
| abstract_inverted_index.increase | 133 |
| abstract_inverted_index.marginal | 132 |
| abstract_inverted_index.markedly | 105 |
| abstract_inverted_index.material | 147 |
| abstract_inverted_index.networks | 30 |
| abstract_inverted_index.proposed | 20 |
| abstract_inverted_index.reducing | 110 |
| abstract_inverted_index.residual | 93 |
| abstract_inverted_index.sampling | 89, 108 |
| abstract_inverted_index.strategy | 90 |
| abstract_inverted_index.training | 136 |
| abstract_inverted_index.Moreover, | 83 |
| abstract_inverted_index.deploying | 28 |
| abstract_inverted_index.equations | 57 |
| abstract_inverted_index.introduce | 85 |
| abstract_inverted_index.leverages | 92 |
| abstract_inverted_index.parameter | 80 |
| abstract_inverted_index.potential | 142 |
| abstract_inverted_index.reduction | 63 |
| abstract_inverted_index.structure | 79 |
| abstract_inverted_index.surpasses | 106 |
| abstract_inverted_index.individual | 39 |
| abstract_inverted_index.minimizing | 34 |
| abstract_inverted_index.parameters | 12 |
| abstract_inverted_index.prediction | 126 |
| abstract_inverted_index.preserving | 38 |
| abstract_inverted_index.principles | 97 |
| abstract_inverted_index.simulation | 148 |
| abstract_inverted_index.subsequent | 16 |
| abstract_inverted_index.viscosity, | 77 |
| abstract_inverted_index.Traditional | 1 |
| abstract_inverted_index.application | 145 |
| abstract_inverted_index.demonstrate | 44 |
| abstract_inverted_index.independent | 29 |
| abstract_inverted_index.outperforms | 48 |
| abstract_inverted_index.parameters. | 121 |
| abstract_inverted_index.strategies, | 109 |
| abstract_inverted_index.Importantly, | 122 |
| abstract_inverted_index.MultiSubPINN | 22, 46, 60 |
| abstract_inverted_index.conventional | 49 |
| abstract_inverted_index.degradation. | 18 |
| abstract_inverted_index.differential | 56 |
| abstract_inverted_index.distribution | 94 |
| abstract_inverted_index.enhancements | 124 |
| abstract_inverted_index.interference | 36 |
| abstract_inverted_index.performance. | 102 |
| abstract_inverted_index.predictions, | 33, 81 |
| abstract_inverted_index.underscoring | 138 |
| abstract_inverted_index.approximately | 113 |
| abstract_inverted_index.architecture, | 9 |
| abstract_inverted_index.physics-based | 88 |
| abstract_inverted_index.respectively. | 82 |
| abstract_inverted_index.significantly | 47 |
| abstract_inverted_index.aforementioned | 120 |
| abstract_inverted_index.optimizations. | 41 |
| abstract_inverted_index.fully-connected | 8 |
| abstract_inverted_index.MultiSubPINN’s | 101, 139 |
| abstract_inverted_index.Physics-Informed | 2 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.56910481 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |