9 articles in this category
I've spent more nights than I'd like chasing phantom solutions in inverse problems. You fit a model, the numbers look clean, but the result is junk. T...
So you've got an inverse problem on your hands. Maybe you're reconstructing an image from noisy projections, or estimating subsurface properties from ...
You've probably seen an MRI scan that looks impossibly clear, or watched a blurry photo get sharpened by an app. Those aren't magic—they're inverse pr...
You've built a beautiful forward model. Maxwell's equations, Navier-Stokes, acoustic wave propagation—the physics is clean, the discretization is stab...
You spend weeks polishing your inverse problem solver. The data's noisy, but your regularization prior is 'well-known'—TV norm, maybe L1 wavelet spars...
You've got a noisy image. Maybe a blurry CT scan, maybe an old photograph. You reach for regularization — Tikhonov, total variation, something to calm...
You spent months designing a prior that encodes physics, smoothness, or sparsity. Then the data arrive—noisy, sparse, maybe corrupted. The posterior l...
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...
Smoothness assumptions are baked into classic regularization. Tikhonov penalizes large derivatives quadratically, which forces solutions to be everywh...