Example of 2D modelfitting using the SasView application 
Cavity size distributions in a steel weldment as derived from SANS
10.1179/1743284714Y.0000000577

Abinitio modelling of polcalcin constrained by SAXS
10.1002/pro.3376 
MC & TAMD modelling of proteins constrained by SANS
10.1016/j.jmgm.2017.02.010  Time evolution of the invariant during crystallisation of P4MP1
10.1038/pj.2012.204

This approach uses iterative optimisation to match the calculated scattering from a model function describing the scattering objects to the measured scattering data. Each iteration one or more physical parameters describing the model (e.g. concentration, size, scattering length density) are adjusted.
 This approach uses mathematical transformations (e.g. Fourier Transforms) to convert the measured scattering data in reciprocalspace (i.e. in Qspace) into a function in realspace. Typical outputs are density correlation functions, volume fraction distributions, and size distributions.
 This approach uses iterative optimisation to match the calculated scattering from assemblies of spheres or from a 3D 'shape envelope function' to the measured scattering data. Each iteration the number and/or position of the spheres, or the curvature of the envelope function, is adjusted.
 This approach uses iterative optimisation in combination with Monte Carlo (MC) and/or Molecular Dynamics (MD) techniques or RRT searches to match a calculated 'atomistic level' structure for the scattering objects to the measured scattering data.
 Other approaches to data analysis may involve identifying, for example: any Qdependencies in the measured data, particular patterns in the Qvalues of any peaks present, asymptotic extrapolations, calculation of the integral under the measured data (the 'invariant'), or the intensity at Q=0.

This approach:
 is easy to learn;
 is good for welldefined scattering objects (e.g. nanoparticles, micelles, vesicles, polymer coils, etc) or combinations of these;
 works best if some a priori information about the scattering objects is available (e.g. shape, approximate size, etc) to guide initial parameter values;
 is generally quick, especially for 1D data (i.e. I(Q) vs Q);
 can work on 2D data (i.e. I(Qx,Qy) vs Qx & Qy);
 can allow cooptimisation of data of different contrasts (or even of SAXS and SANS data);
 can allow for magneticSANS;

This approach:
 is typically used to analyse data from (semi)crystalline polymers, adsorbed polymers, voids/pores in solid materials, or nucleating systems;
 is modelindependent;
 needs highquality data (i.e. good signaltonoise) to transform;
 needs data over a wideQrange (at least 3 decades);

This approach:
 requires an investment in learning to use it well;
 is well developed for solution structural biology, but has been used to simulate micelles, etc;
 does not require a starting structure;
 does not require any force fields;
 ignores any chemistry and physics (there are no chemical bonds, for example);
 can allow for hydration layers;

This approach:
 requires an investment in learning to use it well;
 is presently really only developed for solution structural biology, though a goal is to extend it to more generic soft matter systems;
 requires an 'atomistic' starting structure (e.g. from the PDB or a MD trajectory);
 requires suitable force fields;
 is computeintensive (though there are some cloudbased implementations);
 preserves the chemistry and physics of the structure;
 can allow for hydration layers;

This approach can provide:
 fractal dimensions;
 the type of ordering (fcc, bcc, hcp, etc) in the sample;
 the specific surface area of the sample (S/V);
 persistence lengths;
 osmotic compressibilities;

Software packages for this include:
 SasView
 SASfit
 Scatter 
Software packages for this include:
 (G)IFT*
 Corfunc*
 MAXE
 PRINSAS**
 McSAS
*included in SasView; **obsolete 
Software packages for this include:
 ATSAS Suite
 FoXS

Software packages for this include:
 SASSIE (incl. SCT)*
 MultiFoXS
*also see CCPSAS

Software packages for this include:
 various (incl. spreadsheets) 