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.
Physics-informed neural networks in the recreation of hydrodynamic simulations from dark matter.Mon
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
The paper defines interpretability as model structural transparency and explainability as scientific content mapping, discusses their trade-offs, and frames both as deliberate modeling choices for ML in physics.
A review assessing PINN advances for forward modeling, inverse design, and equation discovery across multi-physics domains.
citing papers explorer
-
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.
-
Interpreting "Interpretability" and Explaining "Explainability" in Machine Learning in Physics
The paper defines interpretability as model structural transparency and explainability as scientific content mapping, discusses their trade-offs, and frames both as deliberate modeling choices for ML in physics.
-
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.