The following pages link to hIPPYlib (Q5025234):
Displaying 34 items.
- hIPPYlib (Q39008) (← links)
- Generalized parallel tempering on Bayesian inverse problems (Q2058888) (← links)
- Derivative-informed projected neural networks for high-dimensional parametric maps governed by PDEs (Q2060092) (← links)
- Goal-oriented a-posteriori estimation of model error as an aid to parameter estimation (Q2083651) (← links)
- Forward and inverse modeling of fault transmissibility in subsurface flows (Q2107210) (← links)
- Randomized maximum likelihood based posterior sampling (Q2130957) (← links)
- Variational Bayesian approximation of inverse problems using sparse precision matrices (Q2138759) (← links)
- Automated finite element solution of diffusion models for image denoising (Q2689385) (← links)
- Statistical Treatment of Inverse Problems Constrained by Differential Equations-Based Models with Stochastic Terms (Q4960988) (← links)
- Stochastic Learning Approach for Binary Optimization: Application to Bayesian Optimal Design of Experiments (Q5071443) (← links)
- Optimal Experimental Design for Inverse Problems in the Presence of Observation Correlations (Q5101014) (← links)
- Taylor Approximation for Chance Constrained Optimization Problems Governed by Partial Differential Equations with High-Dimensional Random Parameters (Q5158925) (← links)
- An Offline-Online Decomposition Method for Efficient Linear Bayesian Goal-Oriented Optimal Experimental Design: Application to Optimal Sensor Placement (Q5886849) (← links)
- Learning physics-based models from data: perspectives from inverse problems and model reduction (Q5887831) (← links)
- A Benchmark for the Bayesian Inversion of Coefficients in Partial Differential Equations (Q6071826) (← links)
- A Bayesian scheme for reconstructing obstacles in acoustic waveguides (Q6084626) (← links)
- Determining the viscosity of the Navier–Stokes equations from observations of finitely many modes (Q6087359) (← links)
- A Fast and Scalable Computational Framework for Large-Scale High-Dimensional Bayesian Optimal Experimental Design (Q6109162) (← links)
- Context-Aware Surrogate Modeling for Balancing Approximation and Sampling Costs in Multifidelity Importance Sampling and Bayesian Inverse Problems (Q6109165) (← links)
- Non-centered parametric variational Bayes’ approach for hierarchical inverse problems of partial differential equations (Q6129005) (← links)
- Bayesian model calibration for diblock copolymer thin film self-assembly using power spectrum of microscopy data and machine learning surrogate (Q6147036) (← links)
- CUQIpy: II. Computational uncertainty quantification for PDE-based inverse problems in Python (Q6149902) (← links)
- Optimal design of chemoepitaxial guideposts for the directed self-assembly of block copolymer systems using an inexact Newton algorithm (Q6158089) (← links)
- Large-scale Bayesian optimal experimental design with derivative-informed projected neural network (Q6159007) (← links)
- Bayesian spatiotemporal modeling for inverse problems (Q6172144) (← links)
- A scalable framework for multi-objective PDE-constrained design of building insulation under uncertainty (Q6185171) (← links)
- Derivative-informed neural operator: an efficient framework for high-dimensional parametric derivative learning (Q6202135) (← links)
- hIPPYlib: An Extensible Software Framework for Large-Scale Inverse Problems Governed by PDEs; Part I: Deterministic Inversion and Linearized Bayesian Inference (Q6324949) (← links)
- Point spread function approximation of high-rank Hessians with locally supported nonnegative integral kernels (Q6543105) (← links)
- Integrated nested Laplace approximations for large-scale spatiotemporal Bayesian modeling (Q6575345) (← links)
- Choosing observation operators to mitigate model error in Bayesian inverse problems (Q6587623) (← links)
- Optimal experimental design: formulations and computations (Q6598420) (← links)
- hIPPYlib-MUQ: a Bayesian inference software framework for integration of data with complex predictive models under uncertainty (Q6601373) (← links)
- Optimizing quantitative photoacoustic imaging systems: the Bayesian Cramér-Rao bound approach (Q6644937) (← links)
