Speaker
Description
Neural network emulators are frequently used to speed up the computation of physical models in physics. However, they generally include only a few input parameters due to the difficulty of generating a dense enough grid of training data pairs in high dimensional parameter spaces. This becomes particularly apparent for cases where they replace physical models that take a long time to compute. We utilize an active learning technique called query by dropout committee to achieve a performance comparable to training data generated on a grid, but with fewer required training examples. We also find that the emulator generalizes better compared to grid-based training: We are able to suppress poor performance which occurs in particular areas of parameter space in grid-based training. Using these methods, we train an emulator on a numerical model of the accretion flow and emission of an accreting disk around a supermassive black hole in order to infer the physical properties of the black hole. Our neural network emulator can approximate the physical simulator to 1% precision or better and achieve 10^4 times speedup over the original model.
