Flow-ABI trains flow-matching models on historical data to produce a set-conditioned functional posterior sampler that delivers near-real-time Bayesian inference for regression and inverse PDE tasks without per-observation optimization.
Scalable physics- informed deep generative model for solving forward and inverse stochastic differential equations.arXiv preprint arXiv:2503.18012, 2025
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
physics.comp-ph 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Flow-based generative models for amortized Bayesian inference in regression and inverse PDE problems
Flow-ABI trains flow-matching models on historical data to produce a set-conditioned functional posterior sampler that delivers near-real-time Bayesian inference for regression and inverse PDE tasks without per-observation optimization.