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B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data

Journal of Computational Physics(2020)10.1016/j.jcp.2020.109913Source: DataRank Database

B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data is a research paper published in Journal of Computational Physics (2020). On theSindex it has a DataRank of 1.0. It has been cited 994 times.

N/A

1.0DataRank · unranked

Scored on demand from live citation data

Open Access994 citations

Free full text

Cite:

Linked data & code

B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data · arXiv

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (1 grants)Impact (FWCI 46.65)OA: greenTopics (35)Linked Datasets (1)DataCite Reuse (1)IDs (PubMed, OpenAlex)

FAIR Checklist

Context only (not used in score)

Findable (2/2)

Has DOI
Indexed in repositories

Accessible (1/2)

Open Access

Interoperable (2/2)

DataCite relations
Linked datasets

Reusable (0/3)

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DataRank Breakdown

Base Score 100%Citation Network 0%

Base Score Contribution

1.0

From this paper's citation signal

Citation Network Contribution

Citation network not refreshed for this result

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Authors (3)

Xuhui Meng,George Em Karniadakis ORCID,Liu Yang ORCID

Live enrichment

PubMed:26207759 OpenAlex:https://openalex.org/W3010849941

National Institutes of Health

Grant: 5U01HL142518-03

Multimodality imaging-driven multifidelity modeling of aortic dissection

FWCI

46.65

Citation Percentile

1.0%

Influential Citations

Citation Trend

2020

2021

2022

2023

2024

2025

2026

greenLicense: Elsevier TDM

repositoryhttps://arxiv.org/pdf/2003.06097

GREENhttps://arxiv.org/pdf/2003.06097

repositoryhttps://arxiv.org/pdf/2003.06097

publisherhttps://api.elsevier.com/content/article/PII:S0021999120306872?httpAccept=text/xml

publisherhttps://api.elsevier.com/content/article/PII:S0021999120306872?httpAccept=text/plain

Fields of Study

Model Reduction and Neural NetworksGaussian Processes and Bayesian InferenceNuclear reactor physics and engineeringComputer ScienceMathematicsPhysics01 natural sciences0103 physical sciences

Keywords

Bayesian probabilityInverse problemArtificial neural networkInverseApplied mathematicsStatistical physicsComputer scienceMathematicsAlgorithmCalculus (dental)Artificial intelligencePhysicsMathematical analysisMedicineGeometryFOS: Computer and information sciencesComputer Science - Machine LearningBayesian physics-informed neural networksBayesian inferenceMachine Learning (stat.ML)noisy dataMachine Learning (cs.LG)nonlinear PDEsStatistics - Machine LearningHamiltonian Monte CarloArtificial neural networks and deep learningvariational inference

B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data

10.48550/arxiv.2003.06097arXiv