Generative models for cosmological field-level inference can reproduce posterior means and cross-correlations yet fail to capture correct uncertainty geometry when validated against HMC reference samples.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
NVAR models exhibit training error scaling laws tied to feature library representation of Lie-series coefficients, with delays reducing one-step error but aiding long-horizon forecasts only under sufficient nonlinearity.
PyCC.id packages a hypothesis-driven method using identifiable ODE skeletons for equation discovery from data, supporting multiple paradigms like neural networks and sparse regression.
citing papers explorer
-
Learning the Universe: Posterior Reliability of Neural Generative Models in High-Dimensional Field-Level Inference of Cosmic Initial Conditions
Generative models for cosmological field-level inference can reproduce posterior means and cross-correlations yet fail to capture correct uncertainty geometry when validated against HMC reference samples.
-
Flow map learning in nonlinear vector autoregressive models: influence of the feature-library structure on the training error
NVAR models exhibit training error scaling laws tied to feature library representation of Lie-series coefficients, with delays reducing one-step error but aiding long-horizon forecasts only under sufficient nonlinearity.
-
PyCC.id: A package for hypothesis-driven equation discovery with structural identifiability
PyCC.id packages a hypothesis-driven method using identifiable ODE skeletons for equation discovery from data, supporting multiple paradigms like neural networks and sparse regression.