Learning noise-induced transitions by multi-scaling reservoir computing Article Swipe
Zequn Lin
,
Zhaofan Lu
,
Zengru Di
,
Ying Tang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1038/s41467-024-50905-w
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1038/s41467-024-50905-w
Noise is usually regarded as adversarial to extracting effective dynamics from time series, such that conventional approaches usually aim at learning dynamics by mitigating the noisy effect. However, noise can have a functional role in driving transitions between stable states underlying many stochastic dynamics. We find that leveraging a machine learning model, reservoir computing, can learn noise-induced transitions. We propose a concise training protocol with a focus on a pivotal hyperparameter controlling the time scale. The approach is widely applicable, including a bistable system with white noise or colored noise, where it generates accurate statistics of transition time for white noise and specific transition time for colored noise. Instead, the conventional approaches such as SINDy and the recurrent neural network do not faithfully capture stochastic transitions even for the case of white noise. The present approach is also aware of asymmetry of the bistable potential, rotational dynamics caused by non-detailed balance, and transitions in multi-stable systems. For the experimental data of protein folding, it learns statistics of transition time between folded states, enabling us to characterize transition dynamics from a small dataset. The results portend the exploration of extending the prevailing approaches in learning dynamics from noisy time series.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41467-024-50905-w
- https://www.nature.com/articles/s41467-024-50905-w.pdf
- OA Status
- gold
- Cited By
- 9
- References
- 55
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401285338
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4401285338Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1038/s41467-024-50905-wDigital Object Identifier
- Title
-
Learning noise-induced transitions by multi-scaling reservoir computingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-08-03Full publication date if available
- Authors
-
Zequn Lin, Zhaofan Lu, Zengru Di, Ying TangList of authors in order
- Landing page
-
https://doi.org/10.1038/s41467-024-50905-wPublisher landing page
- PDF URL
-
https://www.nature.com/articles/s41467-024-50905-w.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.nature.com/articles/s41467-024-50905-w.pdfDirect OA link when available
- Concepts
-
Computer science, Noise (video), Bistability, White noise, Colors of noise, Artificial intelligence, Scaling, Time series, Machine learning, Statistical physics, Noise reduction, Physics, Mathematics, Quantum mechanics, Telecommunications, Image (mathematics), GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
9Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 8, 2024: 1Per-year citation counts (last 5 years)
- References (count)
-
55Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4401285338 |
|---|---|
| doi | https://doi.org/10.1038/s41467-024-50905-w |
| ids.doi | https://doi.org/10.1038/s41467-024-50905-w |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/39097591 |
| ids.openalex | https://openalex.org/W4401285338 |
| fwci | 5.74900694 |
| type | article |
| title | Learning noise-induced transitions by multi-scaling reservoir computing |
| awards[0].id | https://openalex.org/G3289543342 |
| awards[0].funder_id | https://openalex.org/F4320321001 |
| awards[0].display_name | |
| awards[0].funder_award_id | 12105014 |
| awards[0].funder_display_name | National Natural Science Foundation of China |
| biblio.issue | 1 |
| biblio.volume | 15 |
| biblio.last_page | 6584 |
| biblio.first_page | 6584 |
| topics[0].id | https://openalex.org/T12611 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9984999895095825 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1702 |
| topics[0].subfield.display_name | Artificial Intelligence |
| topics[0].display_name | Neural Networks and Reservoir Computing |
| topics[1].id | https://openalex.org/T11206 |
| topics[1].field.id | https://openalex.org/fields/31 |
| topics[1].field.display_name | Physics and Astronomy |
| topics[1].score | 0.984499990940094 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/3109 |
| topics[1].subfield.display_name | Statistical and Nonlinear Physics |
| topics[1].display_name | Model Reduction and Neural Networks |
| topics[2].id | https://openalex.org/T11764 |
| topics[2].field.id | https://openalex.org/fields/13 |
| topics[2].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[2].score | 0.9731000065803528 |
| topics[2].domain.id | https://openalex.org/domains/1 |
| topics[2].domain.display_name | Life Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1311 |
| topics[2].subfield.display_name | Genetics |
| topics[2].display_name | Evolution and Genetic Dynamics |
| funders[0].id | https://openalex.org/F4320321001 |
| funders[0].ror | https://ror.org/01h0zpd94 |
| funders[0].display_name | National Natural Science Foundation of China |
| is_xpac | False |
| apc_list.value | 3920 |
| apc_list.currency | GBP |
| apc_list.value_usd | 4808 |
| apc_paid.value | 3920 |
| apc_paid.currency | GBP |
| apc_paid.value_usd | 4808 |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.7219838500022888 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C99498987 |
| concepts[1].level | 3 |
| concepts[1].score | 0.7056095600128174 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q2210247 |
| concepts[1].display_name | Noise (video) |
| concepts[2].id | https://openalex.org/C97292510 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6667495965957642 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q2304620 |
| concepts[2].display_name | Bistability |
| concepts[3].id | https://openalex.org/C112633086 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5065523386001587 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q381287 |
| concepts[3].display_name | White noise |
| concepts[4].id | https://openalex.org/C114996537 |
| concepts[4].level | 3 |
| concepts[4].score | 0.4887942969799042 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q4854529 |
| concepts[4].display_name | Colors of noise |
| concepts[5].id | https://openalex.org/C154945302 |
| concepts[5].level | 1 |
| concepts[5].score | 0.4513416290283203 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[5].display_name | Artificial intelligence |
| concepts[6].id | https://openalex.org/C99844830 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4466514587402344 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q102441924 |
| concepts[6].display_name | Scaling |
| concepts[7].id | https://openalex.org/C151406439 |
| concepts[7].level | 2 |
| concepts[7].score | 0.41598087549209595 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q186588 |
| concepts[7].display_name | Time series |
| concepts[8].id | https://openalex.org/C119857082 |
| concepts[8].level | 1 |
| concepts[8].score | 0.4026235342025757 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[8].display_name | Machine learning |
| concepts[9].id | https://openalex.org/C121864883 |
| concepts[9].level | 1 |
| concepts[9].score | 0.38278138637542725 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q677916 |
| concepts[9].display_name | Statistical physics |
| concepts[10].id | https://openalex.org/C163294075 |
| concepts[10].level | 2 |
| concepts[10].score | 0.2786521017551422 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q581861 |
| concepts[10].display_name | Noise reduction |
| concepts[11].id | https://openalex.org/C121332964 |
| concepts[11].level | 0 |
| concepts[11].score | 0.15463414788246155 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[11].display_name | Physics |
| concepts[12].id | https://openalex.org/C33923547 |
| concepts[12].level | 0 |
| concepts[12].score | 0.1326778531074524 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[12].display_name | Mathematics |
| concepts[13].id | https://openalex.org/C62520636 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q944 |
| concepts[13].display_name | Quantum mechanics |
| concepts[14].id | https://openalex.org/C76155785 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q418 |
| concepts[14].display_name | Telecommunications |
| concepts[15].id | https://openalex.org/C115961682 |
| concepts[15].level | 2 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q860623 |
| concepts[15].display_name | Image (mathematics) |
| concepts[16].id | https://openalex.org/C2524010 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q8087 |
| concepts[16].display_name | Geometry |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.7219838500022888 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/noise |
| keywords[1].score | 0.7056095600128174 |
| keywords[1].display_name | Noise (video) |
| keywords[2].id | https://openalex.org/keywords/bistability |
| keywords[2].score | 0.6667495965957642 |
| keywords[2].display_name | Bistability |
| keywords[3].id | https://openalex.org/keywords/white-noise |
| keywords[3].score | 0.5065523386001587 |
| keywords[3].display_name | White noise |
| keywords[4].id | https://openalex.org/keywords/colors-of-noise |
| keywords[4].score | 0.4887942969799042 |
| keywords[4].display_name | Colors of noise |
| keywords[5].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[5].score | 0.4513416290283203 |
| keywords[5].display_name | Artificial intelligence |
| keywords[6].id | https://openalex.org/keywords/scaling |
| keywords[6].score | 0.4466514587402344 |
| keywords[6].display_name | Scaling |
| keywords[7].id | https://openalex.org/keywords/time-series |
| keywords[7].score | 0.41598087549209595 |
| keywords[7].display_name | Time series |
| keywords[8].id | https://openalex.org/keywords/machine-learning |
| keywords[8].score | 0.4026235342025757 |
| keywords[8].display_name | Machine learning |
| keywords[9].id | https://openalex.org/keywords/statistical-physics |
| keywords[9].score | 0.38278138637542725 |
| keywords[9].display_name | Statistical physics |
| keywords[10].id | https://openalex.org/keywords/noise-reduction |
| keywords[10].score | 0.2786521017551422 |
| keywords[10].display_name | Noise reduction |
| keywords[11].id | https://openalex.org/keywords/physics |
| keywords[11].score | 0.15463414788246155 |
| keywords[11].display_name | Physics |
| keywords[12].id | https://openalex.org/keywords/mathematics |
| keywords[12].score | 0.1326778531074524 |
| keywords[12].display_name | Mathematics |
| language | en |
| locations[0].id | doi:10.1038/s41467-024-50905-w |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S64187185 |
| locations[0].source.issn | 2041-1723 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2041-1723 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Nature Communications |
| locations[0].source.host_organization | https://openalex.org/P4310319908 |
| locations[0].source.host_organization_name | Nature Portfolio |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310319908, https://openalex.org/P4310319965 |
| locations[0].source.host_organization_lineage_names | Nature Portfolio, Springer Nature |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.nature.com/articles/s41467-024-50905-w.pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Nature Communications |
| locations[0].landing_page_url | https://doi.org/10.1038/s41467-024-50905-w |
| locations[1].id | pmid:39097591 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| 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 | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Nature communications |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/39097591 |
| locations[2].id | pmh:oai:doaj.org/article:9a6703b74cd04aa289227a018717fe4c |
| locations[2].is_oa | False |
| locations[2].source.id | https://openalex.org/S4306401280 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[2].source.host_organization | |
| locations[2].source.host_organization_name | |
| locations[2].license | |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | article |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Nature Communications, Vol 15, Iss 1, Pp 1-10 (2024) |
| locations[2].landing_page_url | https://doaj.org/article/9a6703b74cd04aa289227a018717fe4c |
| locations[3].id | pmh:oai:pubmedcentral.nih.gov:11297999 |
| locations[3].is_oa | True |
| locations[3].source.id | https://openalex.org/S2764455111 |
| locations[3].source.issn | |
| locations[3].source.type | repository |
| locations[3].source.is_oa | False |
| locations[3].source.issn_l | |
| locations[3].source.is_core | False |
| locations[3].source.is_in_doaj | False |
| locations[3].source.display_name | PubMed Central |
| locations[3].source.host_organization | https://openalex.org/I1299303238 |
| locations[3].source.host_organization_name | National Institutes of Health |
| locations[3].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[3].license | other-oa |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | Text |
| locations[3].license_id | https://openalex.org/licenses/other-oa |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | Nat Commun |
| locations[3].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/11297999 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5087961872 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Zequn Lin |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I150229711 |
| authorships[0].affiliations[0].raw_affiliation_string | Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 611731, China. |
| authorships[0].affiliations[1].institution_ids | https://openalex.org/I3133055985 |
| authorships[0].affiliations[1].raw_affiliation_string | Center for Interdisciplinary Studies, Westlake University, Hangzhou, 310024, China. |
| authorships[0].affiliations[2].institution_ids | https://openalex.org/I25254941 |
| authorships[0].affiliations[2].raw_affiliation_string | Department of Systems Science, Faculty of Arts and Sciences, Beijing Normal University, Zhuhai, 519087, China. |
| authorships[0].institutions[0].id | https://openalex.org/I25254941 |
| authorships[0].institutions[0].ror | https://ror.org/022k4wk35 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I25254941 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Beijing Normal University |
| authorships[0].institutions[1].id | https://openalex.org/I150229711 |
| authorships[0].institutions[1].ror | https://ror.org/04qr3zq92 |
| authorships[0].institutions[1].type | education |
| authorships[0].institutions[1].lineage | https://openalex.org/I150229711 |
| authorships[0].institutions[1].country_code | CN |
| authorships[0].institutions[1].display_name | University of Electronic Science and Technology of China |
| authorships[0].institutions[2].id | https://openalex.org/I3133055985 |
| authorships[0].institutions[2].ror | https://ror.org/05hfa4n20 |
| authorships[0].institutions[2].type | education |
| authorships[0].institutions[2].lineage | https://openalex.org/I3133055985 |
| authorships[0].institutions[2].country_code | CN |
| authorships[0].institutions[2].display_name | Westlake University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Zequn Lin |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Center for Interdisciplinary Studies, Westlake University, Hangzhou, 310024, China., Department of Systems Science, Faculty of Arts and Sciences, Beijing Normal University, Zhuhai, 519087, China., Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 611731, China. |
| authorships[1].author.id | https://openalex.org/A5112999392 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Zhaofan Lu |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I25254941 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Systems Science, Faculty of Arts and Sciences, Beijing Normal University, Zhuhai, 519087, China. |
| authorships[1].institutions[0].id | https://openalex.org/I25254941 |
| authorships[1].institutions[0].ror | https://ror.org/022k4wk35 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I25254941 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Beijing Normal University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Zhaofan Lu |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Systems Science, Faculty of Arts and Sciences, Beijing Normal University, Zhuhai, 519087, China. |
| authorships[2].author.id | https://openalex.org/A5102710973 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-5240-298X |
| authorships[2].author.display_name | Zengru Di |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I25254941 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Systems Science, Faculty of Arts and Sciences, Beijing Normal University, Zhuhai, 519087, China. |
| authorships[2].institutions[0].id | https://openalex.org/I25254941 |
| authorships[2].institutions[0].ror | https://ror.org/022k4wk35 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I25254941 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Beijing Normal University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Zengru Di |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Department of Systems Science, Faculty of Arts and Sciences, Beijing Normal University, Zhuhai, 519087, China. |
| authorships[3].author.id | https://openalex.org/A5012587136 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-9272-8570 |
| authorships[3].author.display_name | Ying Tang |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I25254941 |
| authorships[3].affiliations[0].raw_affiliation_string | Department of Systems Science, Faculty of Arts and Sciences, Beijing Normal University, Zhuhai, 519087, China. [email protected]. |
| authorships[3].affiliations[1].institution_ids | https://openalex.org/I150229711 |
| authorships[3].affiliations[1].raw_affiliation_string | Key Laboratory of Quantum Physics and Photonic Quantum Information, Ministry of Education, University of Electronic Science and Technology of China, Chengdu, 611731, China. [email protected]. |
| authorships[3].affiliations[2].institution_ids | https://openalex.org/I150229711 |
| authorships[3].affiliations[2].raw_affiliation_string | Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 611731, China. [email protected]. |
| authorships[3].institutions[0].id | https://openalex.org/I25254941 |
| authorships[3].institutions[0].ror | https://ror.org/022k4wk35 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I25254941 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | Beijing Normal University |
| authorships[3].institutions[1].id | https://openalex.org/I150229711 |
| authorships[3].institutions[1].ror | https://ror.org/04qr3zq92 |
| authorships[3].institutions[1].type | education |
| authorships[3].institutions[1].lineage | https://openalex.org/I150229711 |
| authorships[3].institutions[1].country_code | CN |
| authorships[3].institutions[1].display_name | University of Electronic Science and Technology of China |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Ying Tang |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Department of Systems Science, Faculty of Arts and Sciences, Beijing Normal University, Zhuhai, 519087, China. [email protected]., Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 611731, China. [email protected]., Key Laboratory of Quantum Physics and Photonic Quantum Information, Ministry of Education, University of Electronic Science and Technology of China, Chengdu, 611731, China. [email protected]. |
| has_content.pdf | True |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.nature.com/articles/s41467-024-50905-w.pdf |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Learning noise-induced transitions by multi-scaling reservoir computing |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T12611 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9984999895095825 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1702 |
| primary_topic.subfield.display_name | Artificial Intelligence |
| primary_topic.display_name | Neural Networks and Reservoir Computing |
| related_works | https://openalex.org/W2020047670, https://openalex.org/W2349980442, https://openalex.org/W2607263052, https://openalex.org/W2568148104, https://openalex.org/W2790395016, https://openalex.org/W2147481109, https://openalex.org/W2014204621, https://openalex.org/W2889643344, https://openalex.org/W1966477623, https://openalex.org/W2078944484 |
| cited_by_count | 9 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 8 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 1 |
| locations_count | 4 |
| best_oa_location.id | doi:10.1038/s41467-024-50905-w |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S64187185 |
| best_oa_location.source.issn | 2041-1723 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2041-1723 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Nature Communications |
| best_oa_location.source.host_organization | https://openalex.org/P4310319908 |
| best_oa_location.source.host_organization_name | Nature Portfolio |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310319908, https://openalex.org/P4310319965 |
| best_oa_location.source.host_organization_lineage_names | Nature Portfolio, Springer Nature |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.nature.com/articles/s41467-024-50905-w.pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Nature Communications |
| best_oa_location.landing_page_url | https://doi.org/10.1038/s41467-024-50905-w |
| primary_location.id | doi:10.1038/s41467-024-50905-w |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S64187185 |
| primary_location.source.issn | 2041-1723 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2041-1723 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Nature Communications |
| primary_location.source.host_organization | https://openalex.org/P4310319908 |
| primary_location.source.host_organization_name | Nature Portfolio |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319908, https://openalex.org/P4310319965 |
| primary_location.source.host_organization_lineage_names | Nature Portfolio, Springer Nature |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.nature.com/articles/s41467-024-50905-w.pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Nature Communications |
| primary_location.landing_page_url | https://doi.org/10.1038/s41467-024-50905-w |
| publication_date | 2024-08-03 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W2231913422, https://openalex.org/W1989192823, https://openalex.org/W2145355283, https://openalex.org/W2053574215, https://openalex.org/W4308458589, https://openalex.org/W2092144432, https://openalex.org/W4327569212, https://openalex.org/W2771123011, https://openalex.org/W2022794769, https://openalex.org/W2782714865, https://openalex.org/W2897660243, https://openalex.org/W2852240488, https://openalex.org/W2960828389, https://openalex.org/W2935233765, https://openalex.org/W2103179919, https://openalex.org/W2963003308, https://openalex.org/W3188450053, https://openalex.org/W3169925418, https://openalex.org/W2239232218, https://openalex.org/W2899283552, https://openalex.org/W4288039037, https://openalex.org/W3163993681, https://openalex.org/W2777417212, https://openalex.org/W2064675550, https://openalex.org/W3003257820, https://openalex.org/W2099231026, https://openalex.org/W4311911725, https://openalex.org/W3170977978, https://openalex.org/W4380359760, https://openalex.org/W2118706537, https://openalex.org/W2802795050, https://openalex.org/W3014473050, https://openalex.org/W3152599691, https://openalex.org/W4376276325, https://openalex.org/W4386185673, https://openalex.org/W3046385468, https://openalex.org/W3194202298, https://openalex.org/W4391984793, https://openalex.org/W2786124724, https://openalex.org/W3154850927, https://openalex.org/W3211856448, https://openalex.org/W2796251467, https://openalex.org/W2177933918, https://openalex.org/W2141394518, https://openalex.org/W2982612860, https://openalex.org/W4285255127, https://openalex.org/W4391590458, https://openalex.org/W4392514268, https://openalex.org/W4386880672, https://openalex.org/W3110752711, https://openalex.org/W4283835632, https://openalex.org/W3201489801, https://openalex.org/W3103420451, https://openalex.org/W3098172061, https://openalex.org/W3103145119 |
| referenced_works_count | 55 |
| abstract_inverted_index.a | 32, 49, 61, 66, 69, 82, 180 |
| abstract_inverted_index.We | 45, 59 |
| abstract_inverted_index.as | 5, 114 |
| abstract_inverted_index.at | 20 |
| abstract_inverted_index.by | 23, 149 |
| abstract_inverted_index.do | 121 |
| abstract_inverted_index.in | 35, 154, 193 |
| abstract_inverted_index.is | 2, 78, 137 |
| abstract_inverted_index.it | 92, 164 |
| abstract_inverted_index.of | 96, 131, 140, 142, 161, 167, 188 |
| abstract_inverted_index.on | 68 |
| abstract_inverted_index.or | 88 |
| abstract_inverted_index.to | 7, 175 |
| abstract_inverted_index.us | 174 |
| abstract_inverted_index.For | 157 |
| abstract_inverted_index.The | 76, 134, 183 |
| abstract_inverted_index.aim | 19 |
| abstract_inverted_index.and | 102, 116, 152 |
| abstract_inverted_index.can | 30, 55 |
| abstract_inverted_index.for | 99, 106, 128 |
| abstract_inverted_index.not | 122 |
| abstract_inverted_index.the | 25, 73, 110, 117, 129, 143, 158, 186, 190 |
| abstract_inverted_index.also | 138 |
| abstract_inverted_index.case | 130 |
| abstract_inverted_index.data | 160 |
| abstract_inverted_index.even | 127 |
| abstract_inverted_index.find | 46 |
| abstract_inverted_index.from | 11, 179, 196 |
| abstract_inverted_index.have | 31 |
| abstract_inverted_index.many | 42 |
| abstract_inverted_index.role | 34 |
| abstract_inverted_index.such | 14, 113 |
| abstract_inverted_index.that | 15, 47 |
| abstract_inverted_index.time | 12, 74, 98, 105, 169, 198 |
| abstract_inverted_index.with | 65, 85 |
| abstract_inverted_index.Noise | 1 |
| abstract_inverted_index.SINDy | 115 |
| abstract_inverted_index.aware | 139 |
| abstract_inverted_index.focus | 67 |
| abstract_inverted_index.learn | 56 |
| abstract_inverted_index.noise | 29, 87, 101 |
| abstract_inverted_index.noisy | 26, 197 |
| abstract_inverted_index.small | 181 |
| abstract_inverted_index.where | 91 |
| abstract_inverted_index.white | 86, 100, 132 |
| abstract_inverted_index.caused | 148 |
| abstract_inverted_index.folded | 171 |
| abstract_inverted_index.learns | 165 |
| abstract_inverted_index.model, | 52 |
| abstract_inverted_index.neural | 119 |
| abstract_inverted_index.noise, | 90 |
| abstract_inverted_index.noise. | 108, 133 |
| abstract_inverted_index.scale. | 75 |
| abstract_inverted_index.stable | 39 |
| abstract_inverted_index.states | 40 |
| abstract_inverted_index.system | 84 |
| abstract_inverted_index.widely | 79 |
| abstract_inverted_index.between | 38, 170 |
| abstract_inverted_index.capture | 124 |
| abstract_inverted_index.colored | 89, 107 |
| abstract_inverted_index.concise | 62 |
| abstract_inverted_index.driving | 36 |
| abstract_inverted_index.effect. | 27 |
| abstract_inverted_index.machine | 50 |
| abstract_inverted_index.network | 120 |
| abstract_inverted_index.pivotal | 70 |
| abstract_inverted_index.portend | 185 |
| abstract_inverted_index.present | 135 |
| abstract_inverted_index.propose | 60 |
| abstract_inverted_index.protein | 162 |
| abstract_inverted_index.results | 184 |
| abstract_inverted_index.series, | 13 |
| abstract_inverted_index.series. | 199 |
| abstract_inverted_index.states, | 172 |
| abstract_inverted_index.usually | 3, 18 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.However, | 28 |
| abstract_inverted_index.Instead, | 109 |
| abstract_inverted_index.accurate | 94 |
| abstract_inverted_index.approach | 77, 136 |
| abstract_inverted_index.balance, | 151 |
| abstract_inverted_index.bistable | 83, 144 |
| abstract_inverted_index.dataset. | 182 |
| abstract_inverted_index.dynamics | 10, 22, 147, 178, 195 |
| abstract_inverted_index.enabling | 173 |
| abstract_inverted_index.folding, | 163 |
| abstract_inverted_index.learning | 21, 51, 194 |
| abstract_inverted_index.protocol | 64 |
| abstract_inverted_index.regarded | 4 |
| abstract_inverted_index.specific | 103 |
| abstract_inverted_index.systems. | 156 |
| abstract_inverted_index.training | 63 |
| abstract_inverted_index.asymmetry | 141 |
| abstract_inverted_index.dynamics. | 44 |
| abstract_inverted_index.effective | 9 |
| abstract_inverted_index.extending | 189 |
| abstract_inverted_index.generates | 93 |
| abstract_inverted_index.including | 81 |
| abstract_inverted_index.recurrent | 118 |
| abstract_inverted_index.reservoir | 53 |
| abstract_inverted_index.approaches | 17, 112, 192 |
| abstract_inverted_index.computing, | 54 |
| abstract_inverted_index.extracting | 8 |
| abstract_inverted_index.faithfully | 123 |
| abstract_inverted_index.functional | 33 |
| abstract_inverted_index.leveraging | 48 |
| abstract_inverted_index.mitigating | 24 |
| abstract_inverted_index.potential, | 145 |
| abstract_inverted_index.prevailing | 191 |
| abstract_inverted_index.rotational | 146 |
| abstract_inverted_index.statistics | 95, 166 |
| abstract_inverted_index.stochastic | 43, 125 |
| abstract_inverted_index.transition | 97, 104, 168, 177 |
| abstract_inverted_index.underlying | 41 |
| abstract_inverted_index.adversarial | 6 |
| abstract_inverted_index.applicable, | 80 |
| abstract_inverted_index.controlling | 72 |
| abstract_inverted_index.exploration | 187 |
| abstract_inverted_index.transitions | 37, 126, 153 |
| abstract_inverted_index.characterize | 176 |
| abstract_inverted_index.conventional | 16, 111 |
| abstract_inverted_index.experimental | 159 |
| abstract_inverted_index.multi-stable | 155 |
| abstract_inverted_index.non-detailed | 150 |
| abstract_inverted_index.transitions. | 58 |
| abstract_inverted_index.noise-induced | 57 |
| abstract_inverted_index.hyperparameter | 71 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.94753475 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |