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Abstract
We will discuss about “Bayesian Physics Informed Neural Networks for real-world nonlinear dynamical systems”, Linka, Kevin, et al., Computer Methods in Applied Mechanics and Engineering Volume 402, 1 December 2022, 115346
Abstract
Understanding real-world dynamical phenomena remains a challenging task. Across various scientific disciplines, machine learning has advanced as the go-to technology to analyze nonlinear dynamical systems, identify patterns in big data, and make decision around them. Neural networks are now consistently used as universal function approximators for data with underlying mechanisms that are incompletely understood or
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