Optimal experimental design: Formulations and computations
Questions of 'how best to acquire data'are essential to modelling and prediction in the
natural and social sciences, engineering applications, and beyond. Optimal experimental …
natural and social sciences, engineering applications, and beyond. Optimal experimental …
[HTML][HTML] Remote sensing data assimilation in crop growth modeling from an agricultural perspective: new insights on challenges and prospects
J Wang, Y Wang, Z Qi - Agronomy, 2024 - mdpi.com
The frequent occurrence of global climate change and natural disasters highlights the
importance of precision agricultural monitoring, yield forecasting, and early warning …
importance of precision agricultural monitoring, yield forecasting, and early warning …
Large-scale Bayesian optimal experimental design with derivative-informed projected neural network
We address the solution of large-scale Bayesian optimal experimental design (OED)
problems governed by partial differential equations (PDEs) with infinite-dimensional …
problems governed by partial differential equations (PDEs) with infinite-dimensional …
A note on tools for prediction under uncertainty and identifiability of SIR-like dynamical systems for epidemiology
We provide an overview of the methods that can be used for prediction under uncertainty
and data fitting of dynamical systems, and of the fundamental challenges that arise in this …
and data fitting of dynamical systems, and of the fundamental challenges that arise in this …
Differentiating effects of input aleatory and epistemic uncertainties on system output: A separating sensitivity analysis approach
M Wu, T **ahou, J Chen, Y Liu - Mechanical Systems and Signal …, 2022 - Elsevier
Sensitivity analysis (SA), aiming at identifying and prioritizing the most influential inputs to
output, has been extensively investigated in simulation-based system design under …
output, has been extensively investigated in simulation-based system design under …
Simplified algorithms for adaptive experiment design in parameter estimation
RD McMichael, SM Blakley - Physical review applied, 2022 - APS
Measurements to estimate parameters of a model are commonplace in the physical
sciences, where the traditional approach to automation is to use a sequence of preselected …
sciences, where the traditional approach to automation is to use a sequence of preselected …
Multilevel Monte Carlo estimation of expected information gains
T Goda, T Hironaka, T Iwamoto - Stochastic Analysis and …, 2020 - Taylor & Francis
The expected information gain is an important quality criterion of Bayesian experimental
designs, which measures how much the information entropy about uncertain quantity of …
designs, which measures how much the information entropy about uncertain quantity of …
Sequential infinite-dimensional Bayesian optimal experimental design with derivative-informed latent attention neural operator
We develop a new computational framework to solve sequential Bayesian optimal
experimental design (SBOED) problems constrained by large-scale partial differential …
experimental design (SBOED) problems constrained by large-scale partial differential …
Accelerating Bayesian Optimal Experimental Design with Derivative-Informed Neural Operators
We consider optimal experimental design (OED) for nonlinear Bayesian inverse problems
governed by large-scale partial differential equations (PDEs). For the optimality criteria of …
governed by large-scale partial differential equations (PDEs). For the optimality criteria of …
A two stage Kriging approach for Bayesian optimal experimental design
This paper presents a two-stage Kriging framework designed to efficiently tackle Bayesian
optimal experiment design (OED) problems. To enhance computational efficiency in …
optimal experiment design (OED) problems. To enhance computational efficiency in …