Investigates how uncertainty quantification approaches perform when adapted to geometry-conditioned neural surrogates for CAE, comparing closed-form and sampling-based methods across varying geometries and engineering quantities of interest.
See LICENSE fileneural surrogatesuncertainty quantificationCAE simulation
Proposes test-time trained flow matching for Bayesian filtering, enabling continuous distribution representation for state estimation in high-dimensional physical systems.
See LICENSE filestate estimationflow matchingBayesian filtering
PosteriorBench introduces a benchmark for evaluating how well generative inverse solvers capture true posterior distributions in scientific inverse problems, addressing limitations of pointwise accuracy metrics.
See LICENSE fileinverse problemsgenerative modelsuncertainty quantification
