{"total":19,"rows":[{"id":"2203.05071","title":"On the influence of over-parameterization in manifold based surrogates and deep neural operators","created":"2022-03-09","cited_by_count":29,"area":null},{"id":"2301.02152","title":"L-HYDRA: Multi-Head Physics-Informed Neural Networks","created":"2023-01-05","cited_by_count":29,"area":null},{"id":"2502.11835","title":"Neural Chaos: A Spectral Stochastic Neural Operator","created":"2025-02-17","cited_by_count":21,"area":null},{"id":"2304.07599","title":"Learning in latent spaces improves the predictive accuracy of deep neural operators","created":"2023-04-15","cited_by_count":19,"area":null},{"id":"2403.04670","title":"End-to-end Conditional Robust Optimization","created":"2024-03-07","cited_by_count":17,"area":null},{"id":"2508.20886","title":"Polynomial Chaos Expansion for Operator Learning","created":"2025-08-28","cited_by_count":8,"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":"2202.05063","title":"PCENet: High Dimensional Surrogate Modeling for Learning Uncertainty","created":"2022-02-10","cited_by_count":5,"area":null},{"id":"2503.19333","title":"E-PINNs: Epistemic Physics-Informed Neural Networks","created":"2025-03-25","cited_by_count":4,"area":null},{"id":"2402.04582","title":"Dimensionality reduction can be used as a surrogate model for high-dimensional forward uncertainty quantification","created":"2024-02-07","cited_by_count":2,"area":null},{"id":"2408.03534","title":"Neural active manifolds: nonlinear dimensionality reduction for uncertainty quantification","created":"2024-08-07","cited_by_count":2,"area":null},{"id":"2409.06846","title":"Stratospheric aerosol source inversion: Noise, variability, and uncertainty quantification","created":"2024-09-10","cited_by_count":2,"area":null},{"id":"2410.15721","title":"Learning signals defined on graphs with optimal transport and Gaussian process regression","created":"2024-10-21","cited_by_count":1,"area":null},{"id":"2210.00222","title":"Solving Coupled Differential Equation Groups Using PINO-CDE","created":"2022-10-01","cited_by_count":0,"area":null},{"id":"2401.02630","title":"Model-Agnostic Interpretation Framework in Machine Learning: A Comparative Study in NBA Sports","created":"2024-01-05","cited_by_count":0,"area":null},{"id":"2407.13010","title":"A Resolution Independent Neural Operator","created":"2024-07-17","cited_by_count":0,"area":null},{"id":"2501.12178","title":"Visualizing definitional divergence in high-dimensional data by manifold alignment: Application to 3D right ventricular strain computations","created":"2025-01-21","cited_by_count":0,"area":null},{"id":"2510.22375","title":"Conformalized Polynomial Chaos Expansion for Uncertainty-aware Surrogate Modeling","created":"2025-10-25","cited_by_count":0,"area":null},{"id":"2607.28903","title":"Structured Neural Chaos: An Adaptive Surrogate Modeling Framework for Functional Uncertainty Quantification and Global Sensitivity Analysis","created":"2026-07-31","cited_by_count":0,"area":null}],"next_cursor":null}