Hamiltonian Monte Carlo

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.