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Estimating the symbolic or analytical form of probability density functions (PDFs) from observed samples is a fundamental challenge in statistical and computational modelling. This process is critical for deriving interpretable and generalizable relationships characterizing the underlying phenomenon. Traditionally, thi...
This work proposes ScoreNF, a score-based learning framework built on the Normalizing Flow architecture, integrated with an Independent Metropolis-Hastings (IMH) module, enabling efficient and unbiased sampling from unnormalized target distributions, and shows that ScoreNF maintains high performance even with small tra...
This work introduces an algorithm for learning Boltzmann distributions that does not require any true samples for training and establishes a new method for sample-free learning of complex physical distributions.
SIMANF, a sample free framework that integrates simulated annealing with normalizing flows, and demonstrates its effectiveness on Many-Well distributions and high dimensional Scalar Phi4 lattice field theory distribution.
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