Results 61 to 70 of about 800 (115)

A Bayesian inversion supervised learning framework for the enzyme activity in graphene field-effect transistors

Machine Learning with Applications
Graphene Field-Effect Transistors (GFETs) are gaining prominence in enzyme detection due to their exceptional sensitivity, rapid response, and capability for real-time monitoring of enzymatic reactions.
Ehsan Khodadadian, Samaneh Mirsian, Shahrzad Shashaani, Maryam Parvizi, Amirreza Khodadadian, Peter Knees, Wolfgang Hilber, Clemens Heitzinger   +7 more
doaj   +1 more source

Bayesian Nonparametric Inverse Reinforcement Learning [PDF]

, 2012
Inverse reinforcement learning (IRL) is the task of learning the reward function of a Markov Decision Process (MDP) given the transition function and a set of observed demonstrations in the form of state-action pairs. Current IRL algorithms attempt to find a single reward function which explains the entire observation set.
Bernard Michini, Jonathan P. How
openaire   +3 more sources

Seismic Prediction of Porosity in the Norne Field: Utilizing Support Vector Regression and Empirical Models Driven by Bayesian Linearized Inversion

Applied Sciences
This work aims to improve the characterization of petrophysical properties by accurately estimating subsurface porosity using seismic and well data. The study includes Bayesian Linearized Inversion to obtain elastic parameters (e.g., compressional e ...
Jorge A. Teruya Monroe, Jose J. S. de Figueiredo, Carlos E. S. Amanajas   +2 more
doaj   +1 more source

A Passive Source Location Method in a Shallow Water Waveguide with a Single Sensor Based on Bayesian Theory

Sensors, 2019
Bayesian methodology is a good way to infer unknown parameters in a marine environment. A passive source location method in a shallow water waveguide with a single sensor based on Bayesian theory is presented in this paper.
Xiaoman Li, Shengchun Piao, Minghui Zhang, Yan Liu   +3 more
doaj   +1 more source

Inverse Halftoning Based on Bayesian Theorem [PDF]

, 2009
In this work, a method which can generate high quality inverse halftone images from halftone images is proposed. This method uses least-mean-square (LMS) trained filters to establish the relationship between the current processing position and its corresponding neighbor positions in each kind of halftone image.
Yun-Fu Liu, Jing-Ming Guo, Jiann-Der Lee
openaire   +1 more source

Global Coronal Magnetic Field Estimation Using Bayesian Inference

The Astrophysical Journal
Estimating the magnetic field strength in the solar corona is crucial for understanding different physical processes happening over diverse spatiotemporal scales.
Upasna Baweja, Vaibhav Pant, Iñigo Arregui   +2 more
doaj   +1 more source

Bayesian inverse analysis of unsaturated slope parameters using fine-tuned deep operator network model

Yantu gongcheng xuebao
Bayesian method infers the posterior distribution of slope parameters by combining prior distribution with filed time-series monitoring data. This process requires extensive computational resources due to repeated calls to time-consuming numerical models.
JIE Honghu 1, 2, JIANG Shuihua 1, 2, WAN Jianhong 1, 2, CHANG Zhilu 1, 2, HUANG Jinsong 1, ZHOU Chuangbing 1, 2
doaj   +1 more source

Detection performance and inversion processing of logging-while-drilling extra-deep azimuthal resistivity measurements

Petroleum Science, 2019
We present systematic investigations on the physics, detection performance and inversion of logging-while-drilling extra-deep azimuthal resistivity measurements (EDARM).
Lei Wang, Shao-Gui Deng, Pan Zhang, Ying-Chang Cao, Yi-Ren Fan, Xi-Yong Yuan   +5 more
doaj   +1 more source

A Novel Bayesian Geophysical Inversion Method to Address Loss Function Bias: The Iterative Normalizing Flows Model

Journal of Geophysical Research: Machine Learning and Computation
Geophysical inversion plays a pivotal role in understanding the Earth's internal structure. Recently generative neural networks (GNNs), such as normalizing flows models (NFMs), have gained popularity for solving Bayesian inversion problems.
Binbin Liao, Xiaodong Chen, Jianqiao Xu, Jiangcun Zhou, Heping Sun   +4 more
doaj   +1 more source

QEKI: A Quantum–Classical Framework for Efficient Bayesian Inversion of PDEs

Entropy
Solving Bayesian inverse problems efficiently stands as a major bottleneck in scientific computing. Although Bayesian Physics-Informed Neural Networks (B-PINNs) have introduced a robust way to quantify uncertainty, the high-dimensional parameter spaces ...
Jiawei Yong, Sihai Tang
doaj   +1 more source