Bridging the Simulation-to-Experiment Gap with Generative Models using Adversarial Distribution Alignment

ArXi:2604.01169v1 Announce Type: new A fundamental challenge in science and engineering is the simulation-to-experiment gap. While we often possess prior knowledge of physical laws, these physical laws can be too difficult to solve exactly for complex systems. Such systems are commonly modeled using simulators, which impose computational approximations. Meanwhile, experimental measurements faithfully represent the real world, but experimental data typically consists of observations that only partially reflect the system's full underlying state.