Physics-Informed Emulation of Systemic Circulation for Fast Parameter Estimation and Uncertainty Quantification. [PDF]
This work presents an innovative approach to modelling 1D fluid dynamics in complex networks using physics‐informed neural networks as surrogate models. By integrating physics‐based constraints with data‐driven learning, we develop an efficient and generalisable framework for uncertainty quantification and parameter estimation in real‐world ...
Ryan W +4 more
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VisualPDE: Rapid Interactive Simulations of Partial Differential Equations. [PDF]
Walker BJ +3 more
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From actin waves to mechanism and back: How theory aids biological understanding. [PDF]
Beta C +3 more
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Pattern Formation in Mesic Savannas. [PDF]
Patterson D +3 more
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Nonlinear periodic orbit solutions and their bifurcation structure at the origin of soliton hopping in coupled microresonators. [PDF]
Deshmukh S +4 more
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Deterministic, stochastic, and mean-field PDE models in neuroscience. [PDF]
Çetin C +5 more
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The Emergence of Turing Instability and Pattern Formation in a Nonlinear Stochastic Spatiotemporal Epidemic Model with Reinfections. [PDF]
Singh AK +3 more
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INHERITANCE OF INTRACELLULAR VIRAL RNA IN A MULTISCALE MODEL OF HEPATITIS C INFECTION. [PDF]
Cassidy T +4 more
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Toward Digital Twins for Optimal Radioembolization. [PDF]
Panneerselvam NK +2 more
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Physics-informed neural networks for physiological signal processing and modeling: a narrative review. [PDF]
Zhao A, Fattahi D, Hu X.
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