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An autoencoder compression approach for accelerating large-scale inverse problems
In this work, we propose a close-to-ideal scalable compression approach using autoencoders to eliminate the need for checkpointing and substantial memory storage, thereby reducing both the time-to-solution and memory requirements.
Solving Bayesian Inverse Problems via Variational Autoencoders
Further, this framework possesses an inherent adaptive optimization property that emerges through the learning of the posterior uncertainty.
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