A Physics-Informed neural network (PINN) for parameter identification in analytical ultracentrifugation (AUC) analysis. [PDF]
Cao W, Martin R, Demeler B.
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Forecasting secular variation using physics-informed neural networks for IGRF-14. [PDF]
Shakespeare-Rees N +6 more
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Fractional order modeling of hepatitis C transmission dynamics with physics-informed neural network solutions. [PDF]
Muthupandi V +2 more
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Physics-informed neural network for predicting in vacuo vocal fold eigenmodes: A proof of concept study. [PDF]
Al Khasawneh MEM, Döllinger M, Zhang Z.
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Physics-informed neural network modeling of shock waves by appropriately incorporating equation of state. [PDF]
Mizuno Y, Misaka T, Furukawa Y.
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Automating PINN-based kinematic resolution of robotic joints using robotic process automation frameworks. [PDF]
Agrawal P, Sekar P, Kushwaha KK.
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Physics-Informed Machine Learning for Carbonation Depth Prediction in Concrete. [PDF]
Abbas MM, Bărbulescu A.
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Parameter calibration method for car-following models under snowy weather conditions: Integrating an informer time series encoder and physics-informed neural networks. [PDF]
Sun Y, Li W, Yang M, Zhang X.
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Moving from table to graph in physics-informed spatio-temporal symbolic regression. [PDF]
Lazebnik T, Liberzon A.
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Steel Slag-Enhanced Cement-Stabilized Recycled Aggregate Bases: Mechanical Performance and PINN-Based Sulfate Diffusion Prediction. [PDF]
Zeng G +5 more
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