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Gradient Flows
Abstract
Looking at the theory of gradient flows in spaces which are not necessarily endowed with a natural linear or differentiable structure, this text covers gradient flows in the space of probability measures on a separable Hilbert space, endowed with the Kantorovich-Rubinstein-Wasserstein distance and gradient flows in metric spaces.
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Citations
Cited by
Schiebinger, G., Shu, J., Tabaka, M., Cleary, B., Subramanian, V., Solomon, A., Gould, J., Liu, S., Lin, S., Berube, P., et al. (2019) · Cell · doi:10.1016/j.cell.2019.01.006 · Cited by 829
Scaling algorithms for unbalanced optimal transport problems
Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F.-X. (2018) · Mathematics of Computation · doi:10.1090/mcom/3303 · Cited by 336
Mathematical Models of Overparameterized Neural Networks
Fang, C., Dong, H., & Zhang, T. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2020.3048020 · Cited by 26
Hensel, S., & Laux, T. (2025) · Journal of Differential Geometry · doi:10.4310/jdg/1747065796 · Cited by 25
Cellular gradient flow structure linking single-cell-level rules and population-level dynamics
Horiguchi, S. A., & Kobayashi, T. J. (2023) · Physical Review Research · doi:10.1103/physrevresearch.5.l022052 · Cited by 3
Metadata
- Publisher
- Birkhäuser Basel
- Published
- 2008
- OpenAlex
- W1522579744
- Cited by
- 2,220
- References
- 0
- FWCI
- 67.50
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
