We present a new code and approach, jerald - jax Enhanced Resolution Approximate Lagrangian Dynamics -, that improves on and extends the Lagrangian Deep Learning method of Dai & Seljak (2021), producing high-resolution dark matter, stellar mass and neutral hydrogen maps from lower-resolution approximate N-body simulations. The model is trained using the Sherwood-Relics simulation suite (for a fixed cosmology), specifically designed for the intergalactic medium and the neutral hydrogen distribution in the cosmic web. The output is tested in the redshift range from to and the generalization properties of the learned mapping is demonstrated. jerald produces maps with dark matter, stellar and neutral hydrogen power spectra in excellent agreement with full-hydrodynamical simulations with higher resolution, at large and intermediate scales; in particular, jerald's neutral hydrogen power spectra agree with their higher-resolution full-hydrodynamical counterparts within 90 per cent up to Mpc and within 70 per cent up to Mpc. jerald provides a fast, accurate, and physically motivated approach that we plan to embed in a statistical inference pipeline, such as Simulation-Based Inference, to constrain dark matter properties from large- to intermediate-scale structure observables.
