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Scalable and efficient algorithms for the propagation of uncertainty from data through inference to prediction for large-scale problems, with application to flow of the Antarctic ice sheet
1 references·9 citing·31 second hop
Scalable and efficient algorithms for the propagation of uncertainty from data through inference to prediction for large-scale problems, with application to flow of the Antarctic ice sheet (2015)Scalable and efficient algorithms for the…Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (2008)Climate Change 2007: Impacts,…PyApprox: A software package for sensitivity analysis, Bayesian inference, optimal experimental design, and multi-fidelity uncertainty quantification and surrogate modeling (2023)PyApprox: A software package …Large-Scale Bayesian Optimal Experimental Design with Derivative-Informed Projected Neural Network (2023)Large-Scale Bayesian Optimal …HYPERDIFFERENTIAL SENSITIVITY ANALYSIS IN THE CONTEXT OF BAYESIAN INFERENCE APPLIED TO ICE-SHEET PROBLEMS (2023)HYPERDIFFERENTIAL SENSITIVITY…A Fast and Scalable Computational Framework for Large-Scale High-Dimensional Bayesian Optimal Experimental Design (2023)A Fast and Scalable Computati…Taylor Approximation for Chance Constrained Optimization Problems Governed by Partial Differential Equations with High-Dimensional Random Parameters (2021)Taylor Approximation for Chan…Optimal experimental design for infinite-dimensional Bayesian inverse problems governed by PDEs: a review (2021)Optimal experimental design f…Taylor approximation and variance reduction for PDE-constrained optimal control under uncertainty (2019)Taylor approximation and vari…Analysis of Boundary Effects on PDE-Based Sampling of Whittle--Matérn Random Fields (2019)Analysis of Boundary Effects …Goal-oriented optimal design of experiments for large-scale Bayesian linear inverse problems (2018)Goal-oriented optimal design …On a Measure of the Information Provided by an Experiment (1956)Optimal design of experiments (1993)Bayesian Experimental Design: A Review (1995)Optimal experimental design under irreducible uncertainty for linear inverse problems governed by PDEs (2020)Lectures on Stochastic Programming: Modeling and Theory (2009)Theory and Applications of Robust Optimization (2010)An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach (2011)A scalable framework for multi-objective PDE-constrained design of building insulation under uncertainty (2023)Stochastic Finite Elements: A Spectral Approach (1991)Interpolation of Spatial Data: Some Theory for Kriging (2000)Stochastic Modeling and identification of material parameters on structures produced by additive manufacturing (2021)Stochastic modeling of geometrical uncertainties on complex domains, with application to additive manufacturing and brain interface geometries (2021)Stochastic analysis of geometrically imperfect thin cylindrical shells using topology-aware uncertainty models (2022)Optimum Experimental Designs (1993)HYPER-DIFFERENTIAL SENSITIVITY ANALYSIS FOR NONLINEAR BAYESIAN INVERSE PROBLEMS (2023)Variational Bayesian experimental design for geophysical applications: seismic source location, amplitude versus offset inversion, and estimating CO2 saturations in a subsurface reservoir (2023)A-Optimal Design of Experiments for Infinite-Dimensional Bayesian Linear Inverse Problems with Regularized $\ell_0$-Sparsification (2014)Multifidelity approaches for optimization under uncertainty (2014)A Fast and Scalable Method for A-Optimal Design of Experiments for Infinite-dimensional Bayesian Nonlinear Inverse Problems (2016)SOUPy: Stochastic PDE-constrained optimization under high-dimensional uncertainty in Python (2024)Optimum Experimental Designs (1992)Optimal Design of Experiments (2006)Simulation-based optimal Bayesian experimental design for nonlinear systems (2012)Design and Analysis of Computer Experiments (1989)Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates (2001)Democratizing uncertainty quantification (2024)Uncertainty Quantification for In Silico Chemistry (2026)HYPERDIFFERENTIAL SENSITIVITY ANALYSIS OF UNCERTAIN PARAMETERS IN PDE-CONSTRAINED OPTIMIZATION (2020)The entrepreneur–environment nexus: Uncertainty, innovation, and allocation (2010)OECD Environmental Outlook to 2050 (2011)Determinants of Risk: Exposure and Vulnerability (2012)
this work references cited by second hop

References

  • 2008Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change5,635 cited

Cited by

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  • 2018Goal-oriented optimal design of experiments for large-scale Bayesian linear inverse problems59 cited
  • 2019Analysis of Boundary Effects on PDE-Based Sampling of Whittle--Matérn Random Fields50 cited
  • 2019Taylor approximation and variance reduction for PDE-constrained optimal control under uncertainty42 cited
  • 2023A Fast and Scalable Computational Framework for Large-Scale High-Dimensional Bayesian Optimal Experimental Design33 cited
  • 2023Large-Scale Bayesian Optimal Experimental Design with Derivative-Informed Projected Neural Network27 cited
  • 2021Taylor Approximation for Chance Constrained Optimization Problems Governed by Partial Differential Equations with High-Dimensional Random Parameters21 cited
  • 2023PyApprox: A software package for sensitivity analysis, Bayesian inference, optimal experimental design, and multi-fidelity uncertainty quantification and surrogate modeling19 cited
  • 2023HYPERDIFFERENTIAL SENSITIVITY ANALYSIS IN THE CONTEXT OF BAYESIAN INFERENCE APPLIED TO ICE-SHEET PROBLEMS6 cited

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