{"total":44,"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":"2203.11363","title":"PI-VAE: Physics-Informed Variational Auto-Encoder for stochastic differential equations","created":"2022-03-21","cited_by_count":21,"area":null},{"id":"2502.11835","title":"Neural Chaos: A Spectral Stochastic Neural Operator","created":"2025-02-17","cited_by_count":21,"area":null},{"id":"2202.04648","title":"A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems","created":"2022-02-09","cited_by_count":20,"area":null},{"id":"2402.15115","title":"Physics-constrained polynomial chaos expansion for scientific machine learning and uncertainty quantification","created":"2024-02-23","cited_by_count":17,"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":"2309.01697","title":"Physics-Informed Polynomial Chaos Expansions","created":"2023-09-04","cited_by_count":16,"area":null},{"id":"2203.13908","title":"On efficient algorithms for computing near-best polynomial approximations to high-dimensional, Hilbert-valued functions from limited samples","created":"2022-03-25","cited_by_count":8,"area":null},{"id":"2310.04966","title":"Improved Active Learning via Dependent Leverage Score Sampling","created":"2023-10-08","cited_by_count":6,"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":"2301.13635","title":"Active Learning-based Domain Adaptive Localized Polynomial Chaos Expansion","created":"2023-01-31","cited_by_count":5,"area":null},{"id":"2403.01948","title":"On Fractional Moment Estimation from Polynomial Chaos Expansion","created":"2024-03-04","cited_by_count":4,"area":null},{"id":"2507.21273","title":"Deep Polynomial Chaos Expansion","created":"2025-07-28","cited_by_count":4,"area":null},{"id":"2302.00755","title":"Hierarchical shrinkage Gaussian processes: applications to computer code emulation and dynamical system recovery","created":"2023-02-01","cited_by_count":3,"area":null},{"id":"2311.00553","title":"Polynomial Chaos Surrogate Construction for Random Fields with Parametric Uncertainty","created":"2023-11-01","cited_by_count":3,"area":null},{"id":"2106.05824","title":"Rare event estimation using stochastic spectral embedding","created":"2021-06-09","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":"2206.02523","title":"Sparse Bayesian Learning for Complex-Valued Rational Approximations","created":"2022-06-06","cited_by_count":2,"area":null},{"id":"2412.13731","title":"Reliability analysis for non-deterministic limit-states using stochastic emulators","created":"2024-12-18","cited_by_count":2,"area":null},{"id":"2603.12870","title":"Surrogates for Physics-based and Data-driven Modelling of Parametric Systems: Review and New Perspectives","created":"2026-03-13","cited_by_count":2,"area":null},{"id":"2103.17236","title":"High-Dimensional Uncertainty Quantification via Tensor Regression with Rank Determination and Adaptive Sampling","created":"2021-03-31","cited_by_count":1,"area":null},{"id":"2307.04228","title":"Bayesian tomography using polynomial chaos expansion and deep generative networks","created":"2023-07-09","cited_by_count":1,"area":null},{"id":"2401.06447","title":"Uncertainty-aware multi-fidelity surrogate modeling with noisy data","created":"2024-01-12","cited_by_count":1,"area":null},{"id":"2405.01052","title":"Polynomial Chaos Expanded Gaussian Process","created":"2024-05-02","cited_by_count":1,"area":null},{"id":"2502.06595","title":"Surrogate models for diffusion on graphs via sparse polynomials","created":"2025-02-10","cited_by_count":1,"area":null},{"id":"2505.03246","title":"Bayesian full waveform inversion with sequential surrogate model refinement","created":"2025-05-06","cited_by_count":1,"area":null},{"id":"2505.08683","title":"Uncertainty-Aware Surrogate-based Amortized Bayesian Inference for Computationally Expensive Models","created":"2025-05-13","cited_by_count":1,"area":null},{"id":"2507.12358","title":"Surrogate modeling for uncertainty quantification in nonlinear dynamics","created":"2025-07-16","cited_by_count":1,"area":null},{"id":"2510.03056","title":"Gradient-enhanced global sensitivity analysis with Poincar{\\'e} chaos expansions","created":"2025-10-03","cited_by_count":1,"area":null},{"id":"2510.25036","title":"Bayesian Adaptive Polynomial Chaos Expansions","created":"2025-10-28","cited_by_count":1,"area":null},{"id":"2601.16636","title":"Conformal prediction for full and sparse polynomial chaos expansions","created":"2026-01-23","cited_by_count":1,"area":null},{"id":"2212.14507","title":"Non-intrusive surrogate modelling using sparse random features with applications in crashworthiness analysis","created":"2022-12-30","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":"2507.10303","title":"MF-GLaM: A multifidelity stochastic emulator using generalized lambda models","created":"2025-07-14","cited_by_count":0,"area":null},{"id":"2507.14782","title":"Uncertainty Quantification for Machine Learning-Based Prediction: A Polynomial Chaos Expansion Approach for Joint Model and Input Uncertainty Propagation","created":"2025-07-20","cited_by_count":0,"area":null},{"id":"2509.10825","title":"CUBE: Contrastive Understanding by Balanced Experiments","created":"2025-09-13","cited_by_count":0,"area":null},{"id":"2510.07904","title":"Multi-level informed optimization via decomposed Kriging for large design problems under uncertainty","created":"2025-10-09","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":"2512.10873","title":"Physics-informed Polynomial Chaos Expansion with Enhanced Constrained Optimization Solver and D-optimal Sampling","created":"2025-12-11","cited_by_count":0,"area":null},{"id":"2602.01929","title":"Probabilistic function-on-function nonlinear autoregressive model for emulation and reliability analysis of stochastic dynamical systems","created":"2026-02-02","cited_by_count":0,"area":null},{"id":"2606.17233","title":"Uncertainty Quantification of Engineering Structures by Polynomial Chaos Expansion and Multivariate Active Learning","created":"2026-06-15","cited_by_count":0,"area":null},{"id":"2608.13504","title":"Sparse Orthogonal Regression Technique: A Spectral Framework for Equation Discovery, Approximation, and Integration","created":"2026-08-13","cited_by_count":0,"area":null},{"id":"2608.16126","title":"Coded Hankel Polynomial Chaos: Spectral Identification of Dominant Polynomial-Chaos Modes","created":"2026-08-17","cited_by_count":0,"area":null},{"id":"2609.16606","title":"A Weighted Kernel Method for Approximation that Adapts to Learned Multivariable Structure","created":"2026-09-15","cited_by_count":0,"area":null}],"next_cursor":null}