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Found 3 papers, 2 papers with code
Predicting the Initial Conditions of the Universe using a Deterministic Neural Network
Finding the initial conditions that led to the current state of the universe is challenging because it involves searching over an intractable input space of initial conditions, along with modeling their evolution via tools such as N-body simulations which are computationally expensive.
Field Level Neural Network Emulator for Cosmological N-body Simulations
We build a field level emulator for cosmic structure formation that is accurate in the nonlinear regime.
Simple lessons from complex learning: what a neural network model learns about cosmic structure formation
We find our model generalizes well to these well understood scenarios, demonstrating that the networks have inferred general physical principles and learned the nonlinear mode couplings from the complex, random Gaussian training data.
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