Enhancing high-dimensional probabilistic model updating: A generic generative model-inspired framework with GAN-embedded implementation

doi:10.1016/j.cma.2025.118190

Enhancing high-dimensional probabilistic model updating: A generic generative model-inspired framework with GAN-embedded implementation

2025-07-14

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DOI：10.1016/j.cma.2025.118190

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• An innovative PMU framework that emulates the core principles of generative model is proposed. • Model parameters of the GMM input sampler are embedded in an interpretable network as learnable parameters by reparameterization trick. • A learnable MMD discriminator as the distance metric is devised to achieve a more nuanced measurement of distribution disparity. • Adversarial training enhances the generator’s generative power and the discriminator’s discernment capability. • High-dimensional PMU is performed in an efficient network training manner with stochastic gradient descent.

Enhancing high-dimensional probabilistic model updating: A generic generative model-inspired framework with GAN-embedded implementation

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