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Physics-informed machine learning

Machine learning used to represent physics-based and/or engineering models

Papers

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Showing 161–170 of 192 papers

TitleStatusHype
Kolmogorov n-Widths for Multitask Physics-Informed Machine Learning (PIML) Methods: Towards Robust MetricsCode0
A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse DataCode0
Non-overlapping, Schwarz-type Domain Decomposition Method for Physics and Equality Constrained Artificial Neural NetworksCode0
Viscoelastic Constitutive Artificial Neural Networks (vCANNs) - a framework for data-driven anisotropic nonlinear finite viscoelasticityCode0
Physics-informed machine learning for the COVID-19 pandemic: Adherence to social distancing and short-term predictions for eight countriesCode0
RL for Mitigating Cascading Failures: Targeted Exploration via Sensitivity FactorsCode0
Physics-Informed Machine Learning Method for Large-Scale Data Assimilation ProblemsCode0
PDE-DKL: PDE-constrained deep kernel learning in high dimensionalityCode0
Unsupervised Discovery of Extreme Weather Events Using Universal Representations of Emergent OrganizationCode0
Hyperspectral Blind Unmixing using a Double Deep Image PriorCode0
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