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Enhancing Polynomial Chaos Expansion Based Surrogate Modeling using a Novel Probabilistic Transfer Learning Strategy
For black-box simulations, non-intrusive PCE allows the construction of these surrogates using a set of simulation response evaluations.
Robust scalable initialization for Bayesian variational inference with multi-modal Laplace approximations
In this work, we propose a method for constructing an initial Gaussian mixture model approximation that can be used to warm-start the iterative solvers for variational inference.
A heteroencoder architecture for prediction of failure locations in porous metals using variational inference
In this work we employ an encoder-decoder convolutional neural network to predict the failure locations of porous metal tension specimens based only on their initial porosities.
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