We use transport measurements of an electrostatically-defined quantum dot device in an AlGaAs/GaAs heterostructure to inform and verify our approach. Differences between theory and experiment pervade all of science, and are one of the driving forces of human discovery. To infer the disorder potential we use a combination Simulations often require fewer resources than real experiments of transport measurements and predictions from a physical but rarely capture the full complexity of a system, limiting model. The physical model is an electrostatic simulation from their practical application. Narrowing the gap between which transport features can be estimated. Many simulations a model and the real world is key for the control of complex with different parameter settings are required to compare this systems using machine learning, especially when a machine physical model with transport measurements. To accommodate learning model is trained on a simulation before being applied this need without extreme computation times, we develop to real systems [1, 2]. The reality gap is widened further when a fast approximation of the model using deep learning.