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In contrast to standard stochastic gradient optimization schemes, CSG does not discard old gradient samples from previous iterations. Instead, design dependent integration weights are calculated to form a convex combination as an approximation to the true gradient at the current design. As the approximation error vanishes in the course of the iterations, CSG represents a hybrid approach, starting off like a purely stochastic method and behaving like a full gradient scheme in the limit. In this work, the efficiency of CSG is demonstrated for practically relevant applications from topology optimization. These settings are characterized by both, a large number of optimization variables <jats:italic>and<\/jats:italic> an objective function, whose evaluation requires the numerical computation of multiple integrals concatenated in a nonlinear fashion. Such problems could not be solved by any existing optimization method before. Lastly, with regards to convergence rates, first estimates are provided and confirmed with the help of numerical experiments.\n<\/jats:p>","DOI":"10.1007\/s10589-023-00540-w","type":"journal-article","created":{"date-parts":[[2023,11,24]],"date-time":"2023-11-24T08:02:55Z","timestamp":1700812975000},"page":"977-1008","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["The continuous stochastic gradient method: part II\u2013application and numerics"],"prefix":"10.1007","volume":"87","author":[{"given":"Max","family":"Grieshammer","sequence":"first","affiliation":[]},{"given":"Lukas","family":"Pflug","sequence":"additional","affiliation":[]},{"given":"Michael","family":"Stingl","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0650-3747","authenticated-orcid":false,"given":"Andrian","family":"Uihlein","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,24]]},"reference":[{"issue":"6","key":"540_CR1","doi-asserted-by":"publisher","first-page":"2595","DOI":"10.1007\/s00158-020-02571-x","volume":"61","author":"L Pflug","year":"2020","unstructured":"Pflug, L., Bernhardt, N., Grieshammer, M., Stingl, M.: CSG: a new stochastic gradient method for the efficient solution of structural optimization problems with infinitely many states. 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