Nonlinear inverse problems are usually formulated as optimization problems on function spaces constrained by partial differential equations. As a consequence, in realistic, three-dimensional cases, they become extraordinarily expensive to solve numerically, and advanced methods like adaptive mesh refinement become indispensible. In this contribution, we outline such an adaptive algorithm and demonstrate results using a realistic example from optical tomography.