SZodixStudyAcademic Library
ShelvesOpen access
Study · an open academic library·Metadata from OpenAlex (CC0)·Shelves·OpenAlex metadata, de-duplicated
SOUPy: Stochastic PDE-constrained optimization under high-dimensional uncertainty in Python
4 references·0 citing·17 second hop
SOUPy: Stochastic PDE-constrained optimization under high-dimensional uncertainty in Python (2024)SOUPy: Stochastic PDE-constrained optimiz…Taylor Approximation for Chance Constrained Optimization Problems Governed by Partial Differential Equations with High-Dimensional Random Parameters (2021)Taylor Approximation for Chan…Taylor approximation and variance reduction for PDE-constrained optimal control under uncertainty (2019)Taylor approximation and vari…Optimization of PDEs with Uncertain Inputs (2018)Optimization of PDEs with Unc…Optimization of conditional value-at-risk (2000)Optimization of conditional v…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 Fast and Scalable Computational Framework for Large-Scale High-Dimensional Bayesian Optimal Experimental Design (2023)A scalable framework for multi-objective PDE-constrained design of building insulation under uncertainty (2023)A Stochastic Approximation Method (1951)PORTFOLIO SELECTION* (1952)Coherent Measures of Risk (1999)Optimal experimental design under irreducible uncertainty for linear inverse problems governed by PDEs (2020)Surrogate modeling for efficiently, accurately and conservatively estimating measures of risk (2022)HIGH-DIMENSIONAL STOCHASTIC DESIGN OPTIMIZATION UNDER DEPENDENT RANDOM VARIABLES BY A DIMENSIONALLY DECOMPOSED GENERALIZED POLYNOMIAL CHAOS EXPANSION (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)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)Conditional value-at-risk for general loss distributions (2002)Lectures on Stochastic Programming (2009)Distributionally Robust Optimization Under Moment Uncertainty with Application to Data-Driven Problems (2010)
this work references cited by second hop

References

  • 2000Optimization of conditional value-at-risk6,722 cited
  • 2019Taylor approximation and variance reduction for PDE-constrained optimal control under uncertainty42 cited
  • 2018Optimization of PDEs with Uncertain Inputs25 cited
  • 2021Taylor Approximation for Chance Constrained Optimization Problems Governed by Partial Differential Equations with High-Dimensional Random Parameters21 cited

Cited by

No citing works inside the corpus for this window.

Only edges whose both ends are imported exist here. Widen the year window, or show the whole map. Back to the work