{"total":13,"rows":[{"id":"2305.04933","title":"Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial","created":"2023-05-07","cited_by_count":27,"area":null},{"id":"2102.03935","title":"Latent Map Gaussian Processes for Mixed Variable Metamodeling","created":"2021-02-07","cited_by_count":23,"area":null},{"id":"2107.09814","title":"Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models","created":"2021-07-21","cited_by_count":16,"area":null},{"id":"2102.00327","title":"Learning Interaction Kernels for Agent Systems on Riemannian Manifolds","created":"2021-01-30","cited_by_count":7,"area":null},{"id":"2401.16683","title":"Polynomial Chaos Expansions on Principal Geodesic Grassmannian Submanifolds for Surrogate Modeling and Uncertainty Quantification","created":"2024-01-30","cited_by_count":6,"area":null},{"id":"2503.03963","title":"Generative Learning of Densities on Manifolds","created":"2025-03-05","cited_by_count":4,"area":null},{"id":"2110.02296","title":"On the Correspondence between Gaussian Processes and Geometric Harmonics","created":"2021-10-05","cited_by_count":3,"area":null},{"id":"2407.21435","title":"Transient anisotropic kernel for probabilistic learning on manifolds","created":"2024-07-31","cited_by_count":3,"area":null},{"id":"2106.07908","title":"Machine learning-based conditional mean filter: a generalization of the ensemble Kalman filter for nonlinear data assimilation","created":"2021-06-15","cited_by_count":2,"area":null},{"id":"2202.05112","title":"Probabilistic learning inference of boundary value problem with uncertainties based on Kullback-Leibler divergence under implicit constraints","created":"2022-02-10","cited_by_count":2,"area":null},{"id":"2205.03078","title":"Probabilistic learning constrained by realizations using a weak formulation of Fourier transform of probability measures","created":"2022-05-06","cited_by_count":1,"area":null},{"id":"2506.02254","title":"Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps","created":"2025-06-02","cited_by_count":0,"area":null},{"id":"2512.10407","title":"Supervised Learning of Random Neural Architectures Structured by Latent Random Fields on Compact Boundaryless Multiply-Connected Manifolds","created":"2025-12-11","cited_by_count":0,"area":null}],"next_cursor":null}