PINN-based joint reconstruction of H(z) and fσ8(z) coupled through the GR growth equation recovers the input H0 prior exactly, yields fσ8(z) below ΛCDM at all redshifts, and shows Om(z) departure from flat ΛCDM at low z.
Unraveling particle dark matter with Physics- Informed Neural Networks,
2 Pith papers cite this work. Polarity classification is still indexing.
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A review assessing PINN advances for forward modeling, inverse design, and equation discovery across multi-physics domains.
citing papers explorer
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Joint reconstruction of $H(z)$ and $f\sigma_8(z)$ with physics informed neural networks
PINN-based joint reconstruction of H(z) and fσ8(z) coupled through the GR growth equation recovers the input H0 prior exactly, yields fσ8(z) below ΛCDM at all redshifts, and shows Om(z) departure from flat ΛCDM at low z.
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Beyond Data-Driven: How Physics-Informed Neural Networks are Reshaping Multi-Physics Design and Discovery
A review assessing PINN advances for forward modeling, inverse design, and equation discovery across multi-physics domains.