romtools.workflows.inverse

Inverse workflows estimate unknown model parameters from observed data.

An inverse problem starts from a forward model, experimental or synthetic observations, and a parameterized uncertainty model. The goal is to identify parameter values, or a posterior distribution over parameter values, that make the model predictions consistent with the observations.

romtools currently supports:

• Ensemble Kalman inversion (EKI) for derivative-free parameter calibration.

• Multifidelity EKI with control variates and adaptive reduced-order model refresh strategies.

• Variational inference (VI) with gradient, Adam, and Newton optimizers for Gaussian variational families, including diagonal and full-covariance VI.

• Multifidelity VI with control variates and adaptive reduced-order model updates, including full-covariance natural-coordinate Newton optimization.

Modules

bfgs_drivers
ego_drivers	Single-fidelity efficient global optimization drivers.
ego_optimization_methods	Optimization methods and configs used by inverse EGO workflows.
eki_bound_transforms	Bound-handling adapters for EKI and MF-EKI.
eki_drivers	Single-fidelity ensemble Kalman inversion drivers.
full_covariance_auto_mf_vi	Auto-ROM convenience wrapper for full-covariance MF-VI.
full_covariance_lagged_newton	Lagged-curvature support for full-covariance natural Newton VI/MF-VI.
full_covariance_mf_vi_drivers	Full-covariance multifidelity variational inference.
full_covariance_newton	Natural-coordinate Newton support for full-covariance Gaussian VI/MF-VI.
full_covariance_router	Public routing for Gaussian VI variational families.
full_covariance_vi	Utilities for full-covariance Gaussian variational inference.
full_covariance_vi_drivers	Full-covariance Gaussian VI wrappers.
mf_eki_drivers	Multifidelity ensemble Kalman inversion drivers.
mf_vi_drivers	Multifidelity variational inference drivers.
mf_vi_rom_substeps	ROM-only optimizer substeps for multifidelity variational inference.
vi_default_policy	Centralized public defaults for VI and MF-VI optimization.
vi_drivers	Variational inference drivers for Gaussian posterior approximations.
vi_optimization_methods	Optimization methods and configs used by inverse VI workflows.
vi_run_directory_policy	Run-directory policy for VI and MFVI model evaluations.
vi_sample_reuse	Importance-sampling sample reuse for VI and MFVI.
vi_sample_reuse_hessian	Newton/Hessian support for importance-sampling VI sample reuse.