| Description: Physics is replete with high-fidelity simulators, computational manifestations of physical theories. These simulators often incorporate experimental data or are composed of disparate phenomena that occur at different scales or regimes. Ironically, while these simulators provide our highest-fidelity physical models, they are not well suited for inferring properties of the model from data. I will formulate the emerging area of simulation-based inference and describe how machine learning techniques are well-suited for this task. Finally, I will provide examples of how these techniques can impact physics at the Large Hadron Collider, astroparticle physics, lattice field theory, and molecular dynamics. |