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We present an extension of our recently developed Wasserstein optimized modelto emulate accurate high-resolution features from computationally cheaperlow-resolution cosmological simulations. Our deep physical modelling techniquerelies on restricted neural networks to perform a mapping of the distributionof the low-resolution cosmic density field to the space of the high-resolutionsmall-scale structures. We constrain our network using a single triplet ofhigh-resolution initial conditions and the corresponding low- andhigh-resolution evolved dark matter simulations from the Quijote suite ofsimulations. We exploit the information content of the high-resolution initialconditions as a well constructed prior distribution from which the networkemulates the small-scale structures. Once fitted, our physical model yieldsemulated high-resolution simulations at low computational cost, while alsoproviding some insights about how the large-scale modes affect the small-scalestructure in real space.

Super-resolution emulator of cosmological simulations using deep physical models