1 مستودع
Transforms data values to zero mean and unit variance using Gaussian statistics.
Distinct from Data Normalization: Focuses on statistical value normalization for ML, unlike schema unification in Data Normalization
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Neuraloperator is a library for learning mappings between infinite-dimensional function spaces, serving as a tool to accelerate physics simulations and partial differential equation solving. It implements resolution-invariant models and spectral neural networks that can produce consistent predictions regardless of the input grid resolution or spatial discretization. The framework incorporates physics-informed neural networks that enforce physical constraints and differential equations through specialized loss functions. It utilizes Fourier transforms and spectral projections to process multid
Provides Gaussian normalizers to transform spatial data to zero mean and unit variance.