Inverse Problems & Regularization

Inverse Problems & Regularization

When Your Prior Dominates the Data: Bayesian Regularization in Low-SNR Regimes

You spent months designing a prior that encodes physics, smoothness, or sparsity. Then the data arrive—noisy, sparse, maybe corrupted. The posterior l...

Inverse Problems & Regularization

Choosing the Regularization Parameter When the L-Curve Lies

You spend hours computing the L-curve. The corner looks clear—perfect trade-off between residual and solual norm. You pick that lambda. But the recons...

Inverse Problems & Regularization

Why Tikhonov Fails for Discontinuous Solutions (And What to Reach For Instead)

Smoothness assumptions are baked into classic regularization. Tikhonov penalizes large derivatives quadratically, which forces solutions to be everywh...