MRS Bulletin’s Impact editor Markus Buehler interviews Huajian Gao of Nanyang Technological University, Singapore and Bo Ni of Brown University on their development of a deep learning method to predict the elastic modulus field based on strain data that may be the result of an experiment. The method is highly efficient and offers real-time solutions to problems that usually require complex numerical methods that rely on variational methods to solve elasticity problems, like finite element analysis. This type of approach may change the way researchers interpret experimental data. See the article “A deep learning approach to the inverse problem of modulus identification in elasticity” (doi:10.1557/mrs.2020.231).