Manifold learning for coherent design interpolation based on geometrical and topological descriptors
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Publication:2683444
DOI10.1016/J.CMA.2022.115859OpenAlexW4316096672MaRDI QIDQ2683444
Publication date: 10 February 2023
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.cma.2022.115859
machine learningdimensionality reductionoptimal transporttopological data analysisstructural optimisationlocally linear embedding
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Cites Work
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- The finite cell method: a review in the context of higher-order structural analysis of CAD and image-based geometric models
- Accurate recovery-based upper error bounds for the extended finite element framework
- The finite cell method for three-dimensional problems of solid mechanics
- Finite cell method. \(h\)- and \(p\)-extension for embedded domain problems in solid mechanics
- Computing persistent homology
- Principal component analysis.
- Mathematical Methods of Organizing and Planning Production
- On the Translocation of Masses
- Convolutional wasserstein distances
- CutFEM: Discretizing geometry and partial differential equations
- A recovery-type error estimator for the extended finite element method based onsingular+smoothstress field splitting
- Topology and data
- Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
- An Introduction to Variational Autoencoders
- Persistence Images: A Stable Vector Representation of Persistent Homology
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