A new amortized Bayesian inversion method trains a derivative-informed neural surrogate of the parameter-to-observable map and then uses it to optimize a lazy transport map in a low-dimensional latent space.
A note on the relationship between PDE-based precision operators and Mat\'ern covariances
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abstract
The purpose of this technical note is to summarize the relationship between the marginal variance and correlation length of a Gaussian random field with Mat\'ern covariance and the coefficients of the corresponding partial-differential-equation (PDE)-based precision operator.
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math.NA 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
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LazyDINO: Fast, scalable, and efficiently amortized Bayesian inversion via structure-exploiting and surrogate-driven measure transport
A new amortized Bayesian inversion method trains a derivative-informed neural surrogate of the parameter-to-observable map and then uses it to optimize a lazy transport map in a low-dimensional latent space.