Results 31 to 40 of about 2,849 (140)

Geometric ergodicity of the Random Walk Metropolis with position-dependent proposal covariance [PDF]

, 2015
We consider a Metropolis-Hastings method with proposal kernel $\mathcal{N}(x,hG^{-1}(x))$, where $x$ is the current state. After discussing specific cases from the literature, we analyse the ergodicity properties of the resulting Markov chains.
Livingstone, Samuel
core   +2 more sources

Long-Run Accuracy of Variational Integrators in the Stochastic Context [PDF]

, 2010
This paper presents a Lie-Trotter splitting for inertial Langevin equations (Geometric Langevin Algorithm) and analyzes its long-time statistical properties. The splitting is defined as a composition of a variational integrator with an Ornstein-Uhlenbeck
Bou-Rabee N., Houman Owhadi, Nawaf Bou-Rabee, Talay D.   +3 more
core   +3 more sources

Efficient Gaussian Sampling for Solving Large-Scale Inverse Problems using MCMC Methods [PDF]

, 2014
The resolution of many large-scale inverse problems using MCMC methods requires a step of drawing samples from a high dimensional Gaussian distribution.
Gilavert, Clément, Idier, Jérôme, Moussaoui, Saïd   +2 more
core   +4 more sources

Modelling Spatial Compositional Data: Reconstructions of past land cover and uncertainties [PDF]

, 2017
In this paper, we construct a hierarchical model for spatial compositional data, which is used to reconstruct past land-cover compositions (in terms of coniferous forest, broadleaved forest, and unforested/open land) for five time periods during the past
Gaillard, Marie-José, Lindström, Johan, Pirzamanbein, Behnaz, Poska, Anneli   +3 more
core   +2 more sources

A High-Dimensional Parameter Identification Method for Pipelines Based on Static Strain and DNN Surrogate Models to Accelerate Langevin Bayesian Inference

Buildings
This study develops a Bayesian parameter identification framework that uses static strain measurements to update pipeline structural models under complex boundary conditions.
Li Chen, Zhifeng Wu, Yanwen Liu, Zhiyong Li   +3 more
doaj   +1 more source

A deep operator network for Bayesian parameter identification of self-oscillators

Data-Centric Engineering
Many physical systems exhibit limit-cycle oscillations that can typically be modeled as stochastically driven self-oscillators. In this work, we focus on a self-oscillator model where the nonlinearity is on the damping term.
Tobias Sugandi, Bayu Dharmaputra, Nicolas Noiray   +2 more
doaj   +1 more source

Role of Wadsley Defects and Cation Disorder to Enhance MoNb12O33 Diffusion

Advanced Energy Materials, Volume 16, Issue 12, 25 March 2026.
Wadsley‐Roth niobates are high‐rate capable and durable anode materials for lithium‐ion batteries. Defect‐tailoring of MoNb12O33 is shown to substantially enhance lithium diffusion. Computational models were used to separate the effects of cation disorder and Wadsley defects to identify that both led to the occupation of fast diffusion sites at lower ...
CJ Sturgill, Manish Kumar, Nima Karimitari, Iva Milisavljevic, Coby S. Collins, Aaron Hegler, Hsin‐Yun Joy Chao, Santosh Kiran Balijepalli, Scott T. Misture, Christopher Sutton, Morgan Stefik   +10 more
wiley   +1 more source

Uncertainty quantification of CT regularized reconstruction within the Bayesian framework

e-Journal of Nondestructive Testing
Computed Tomography (CT) reconstruction is an important inverse problem in industrial imaging, requiring robust methods to address different sources of error in the data and model.
Negin Khoeiniha, Patricio Guerrero, Wim Dewulf   +2 more
doaj   +1 more source

Field‐Scale Soil Moisture Predictions in Real Time Using In Situ Sensor Measurements in an Inverse Modeling Framework: SWIM2

Water Resources Research, Volume 62, Issue 2, February 2026.
Abstract Affordable autonomous soil sensors and IoT technology enable real‐time soil moisture monitoring, which offers opportunities for real‐time model calibration and irrigation optimization. We introduce an irrigation decision support system SWIM2 (Sensor Wielded Inverse Modeling of a Soil Water Irrigation Model), a digital twin that integrates ...
Marit G. A. Hendrickx, Jan Vanderborght, Pieter Janssens, Eric Laloy, Sander Bombeke, Evi Matthyssen, Anne Waverijn, Jan Diels   +7 more
wiley   +1 more source

Sampling from Rough Energy Landscapes [PDF]

, 1905
We examine challenges to sampling from Boltzmann distributions associated with multiscale energy landscapes. The multiscale features, or "roughness," corresponds to highly oscillatory, but bounded, perturbations of a smooth landscape.
Plecháč, Petr, Simpson, Gideon
core   +4 more sources