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 and Newton optimizers for Gaussian variational families.

• Multifidelity VI with control variates and adaptive reduced-order model updates.

Modules

bfgs_drivers
eki_drivers	Single-fidelity ensemble Kalman inversion drivers.
mf_eki_drivers	Multifidelity ensemble Kalman inversion drivers.
mf_vi_drivers	Multifidelity variational inference drivers.
vi_drivers	Variational inference drivers for Gaussian posterior approximations.
vi_optimization_methods	Optimization methods and configs used by inverse VI workflows.