Physics-informed neural networks in the recreation of hydrodynamic simulations from dark matter

Zhenyu Dai*, Ben Moews, Ricardo Vilalta, Romeel Davé

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Physics-informed neural networks have emerged as a coherent framework for building predictive models that combine statistical patterns with domain knowledge. The underlying notion is to enrich the optimization loss function with known relationships to constrain the space of possible solutions. Hydrodynamic simulations are a core constituent of modern cosmology, while the required computations are both expensive and time-consuming. At the same time, the comparatively fast simulation of dark matter requires fewer resources, which has led to the emergence of machine learning algorithms for baryon inpainting as an active area of research; here, recreating the scatter found in hydrodynamic simulations is an ongoing challenge. This paper presents the first application of physics-informed neural networks to baryon inpainting by combining advances in neural network architectures with physical constraints, injecting theory on baryon conversion efficiency into the model loss function. We also introduce a punitive prediction comparison based on the Kullback-Leibler divergence, which enforces scatter reproduction. By simultaneously extracting the complete set of baryonic properties for the simba suite of cosmological simulations, our results demonstrate improved accuracy of baryonic predictions based on dark matter halo properties and successful recovery of the fundamental metallicity relation, and retrieve scatter that traces the target simulation's distribution.

Original languageEnglish
Pages (from-to)3381-3394
Number of pages14
JournalMonthly Notices of the Royal Astronomical Society
Issue number2
Early online date3 Nov 2023
Publication statusPublished - 1 Jan 2024

Keywords / Materials (for Non-textual outputs)

  • galaxies: evolution
  • galaxies: haloes
  • methods: analytical
  • methods: statistical


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