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Found 2 papers, 1 papers with code
Active Learning for Saddle Point Calculation
To reduce the number of expensive computations of the true gradients, we propose an active learning framework consisting of a statistical surrogate model, Gaussian process regression (GPR) for the energy function, and a single-walker dynamics method, gentle accent dynamics (GAD), for the saddle-type transition states.
Adaptive Gaussian process approximation for Bayesian inference with expensive likelihood functions
In particular, we write the joint density approximately as a product of an approximate posterior density and an exponentiated GP surrogate.
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