Computational methods are an ideal complement for inferring non directly measurable quantities such as forces or growth distribution. I will present a continuum approach where growth tensor is deduced with different objectives: for computing optimal locomotion of soft bodies on non-isotropic substrates, and for inferring the growth distribution that best matches a set of experimental measurements. Importantly, the uncertainty on the boundary conditions is also included in the inverse analysis, jointly with an iterative regularisation process. The methodology is applied  to different worm locomotion strategies, and axolotl limb growth. In each case it is revealed that non-isotropic friction and growth pattern are necessary conditions for reproducing experimental motions and deformations.

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