science1 publisher
A Bayesian PDE inference framework swaps adjoint solves for LoRA fine-tuning
Uncertainty quantification on PDE inverse problems normally costs either an adjoint solve or a large offline training set. A Nature Communications paper reports a sampler whose gradients come from a small local fine-tune instead.
Publishers:nature.com
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