  We present a new ansatz which can successfully be used to determine the
morphological properties of the supercluster-void network. The ansatz is based
on a surface modelling scheme SURFGEN, which generates a triangulated surface
from a discrete data set representing (say) the distribution of galaxies in
real (or redshift) space. Four Minkowski functionals -- surface area, volume,
extrinsic curvature and genus -- describe the geometry and topology of the
supercluster-void network. Ratio's of Minkowski functionals -- Shapefinders --
provide us with an excellent diagnostic of three dimensional shapes of
clusters, superclusters and voids. Minkowski functionals and Shapefinders are
determined for a triangulated iso-density surface using SURFGEN. SURFGEN is
tested against both simply and multiply connected eikonal surfaces such as
triaxial ellipsoids and tori. Remarkably, the first three Minkowski functionals
are computed to better than 1% accuracy while the fourth (genus) is known
exactly. SURFGEN also gives excellent results when applied to Gaussian random
fields. Our results indicate that the surface modelling scheme SURFGEN is
accurate and robust and can successfully be used to quantify the topology and
morphology of the supercluster-void network in the universe. We apply SURFGEN
to three cosmological models, $\L$CDM, $\T$CDM and SCDM and obtain interesting
new results pertaining to the geometry, morphology and topology of large scale
structure.
