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Found 6 papers, 3 papers with code
Diffusion-HMC: Parameter Inference with Diffusion Model driven Hamiltonian Monte Carlo
This work uses a single diffusion generative model to address these interlinked objectives -- as a surrogate model or emulator for cold dark matter density fields conditional on input cosmological parameters, and as a parameter inference model that solves the inverse problem of constraining the cosmological parameters of an input field.
Quantum Many-Body Physics Calculations with Large Language Models
We evaluate GPT-4's performance in executing the calculation for 15 research papers from the past decade, demonstrating that, with correction of intermediate steps, it can correctly derive the final Hartree-Fock Hamiltonian in 13 cases and makes minor errors in 2 cases.
Cosmological Field Emulation and Parameter Inference with Diffusion Models
Cosmological simulations play a crucial role in elucidating the effect of physical parameters on the statistics of fields and on constraining parameters given information on density fields.
Probabilistic reconstruction of Dark Matter fields from biased tracers using diffusion models
Galaxies are biased tracers of the underlying cosmic web, which is dominated by dark matter components that cannot be directly observed.
Can denoising diffusion probabilistic models generate realistic astrophysical fields?
In this work we investigate the ability of these models to generate fields in two astrophysical contexts: dark matter mass density fields from cosmological simulations and images of interstellar dust.
Contrastive Similarity Matching for Supervised Learning
We propose a novel biologically-plausible solution to the credit assignment problem motivated by observations in the ventral visual pathway and trained deep neural networks.
