We present an approach for compressing volumetric scalar fields using implicit neural representations. Our represents a field as learned function, wherein network maps point in the domain to output value. By setting number of weights be smaller than input size, we achieve compressed representations fields, thus framing compression type function approximation. Combined with carefully quantizing ...

Abstract We examine long-wavelength correlation functions of massive scalar fields in de Sitter spacetime. For the theory with a quartic self-interaction, two-point function is calculated up to two loops. Comparing our results Hartree-Fock approximation and stochastic approach shows that former resums only cactus type diagrams, whereas latter contains sunset diagram as well produces correct res...