Results 171 to 180 of about 484,217 (355)

Exact simulation of jump-diffusion processes with Monte Carlo applications [PDF]

, 2008
We introduce a novel algorithm (JEA) to simulate exactly from a class of one-dimensional jump-diffusion processes with state-dependent intensity. The simulation of the continuous component builds on the recent Exact Algorithm ((1)).
Casella, Bruno, Roberts, Gareth O.
core  

A Phase‐Resolved Geometric Deep Learning Framework Maps Structural Determinants of Disease‐Associated Protein Aggregation and Guides Suppressor Design

Advanced Science, EarlyView.
SKALE 2.0 maps disease‐associated protein aggregation as a phase‐resolved structural process, linking mutation‐induced geometric perturbations to nucleation, elongation, and suppressor design. Across neurodegenerative proteins, the framework reveals cryptic aggregation vulnerabilities, separates phase‐concordant and phase‐switching mutations, and ...
Jia Shen Sio, Wei Xuan Wilson Loo, Yan Shan Loo, Wen Xin Tan, Hui Xuan Lim, Huitao Liu, Chen Seng Ng   +6 more
wiley   +1 more source

Bayesian Adaptive Hamiltonian Monte Carlo with an Application to High-Dimensional BEKK GARCH Models [PDF]

Hamiltonian Monte Carlo (HMC) is a recent statistical procedure to sample from complex distributions. Distant proposal draws are taken in a equence of steps following the Hamiltonian dynamics of the underlying parameter space, often yielding superior ...
John Maheu, Martin Burda
core   +2 more sources

An efficient quasi-Monte Carlo method with forced fixed detection for photon scatter simulation in CT. [PDF]

PLoS One, 2023
Lin G, Deng S, Wang X.
europepmc   +1 more source

Taming Flexibility: Synergistic Pore and Polarity Engineering in a MOF for High‐Efficiency Xe/Kr Separation

Advanced Science, EarlyView.
Through a mixed‐ligand strategy that precisely regulates pore size and framework polarity, the Xe adsorption behavior is transformed from flexible to near‐rigid. ZIF‐7‐Cl(20) achieves sensitive recognition, efficient capture, and high selectivity for Xe, enabling high‐efficiency separation from Xe/Kr mixtures.
Tao Zhao, Xue Wang, Youjin Gong, Siqi Dong, Shunshun Xiong, Hui Xu, Aziz Bakhtiyarovich Ibragimov, Thamraa Alshahrani, He Zheng, Lin Li, Junkuo Gao   +10 more
wiley   +1 more source

Polarization Dynamics in Ferroelectrics: Insights Enabled by Machine Learning Molecular Dynamics

Advanced Science, EarlyView.
Machine learning molecular dynamics is presented as a route to capture polarization switching, domain wall kinetics, topological polar textures, and polar mechanical coupling beyond the limits of conventional atomistic methods. This Perspective surveys recent progress and identifies key methodological directions, including long‐range electrostatics ...
Dongyu Bai, Ri He, Junxian Liu, Liangzhi Kou   +3 more
wiley   +1 more source

29. MONTE CARLO TECHNIQUES

, 2008
Monte Carlo techniques are often the only practical way to evaluate difficult integrals or to sample random variables governed by complicated probability density functions.
Monte Carlo Techniques
core  

Fundamental Challenges, Physical Implementations, and Integration Strategies for Ising Machines in Large‐Scale Optimization Tasks

Advanced Electronic Materials, EarlyView.
Ising machines are emerging as specialized hardware solvers for computationally hard optimization problems. This review examines five major platforms—digital CMOS, analog CMOS, emerging devices, coherent optics, and quantum systems—highlighting physics‐rooted advantages and shared bottlenecks in scalability and connectivity.
Hyunjun Lee, Joon Pyo Kim, Sanghyeon Kim
wiley   +1 more source

Monte Carlo method for assessment of a multimodal insertable biosensor. [PDF]

J Biomed Opt, 2022
Fine J, McShane MJ, Coté GL.
europepmc   +1 more source

Efficient In‐Hardware Matrix–Vector Multiplication and Addition Exploiting Bilinearity of Schottky Barrier Transistors Processed on Industrial FDSOI

Advanced Electronic Materials, EarlyView.
ABSTRACT Machine learning and Artificial Intelligence (AI) tasks have stretched traditional hardware to its limits. In‐hardware computation is a novel approach that aims to run complex operations, such as matrix–vector multiplication, directly at the device level for increased efficiency.
Juan P. Martinez, Niladri Bhattacharjee, Yuxuan He, Giulio Galderisi, Brojogopal Sapui, Sara Mannaa, Ian O'Connor, Violetta Sessi, Peter Javorka, Thomas Mikolajick, Jens Trommer   +10 more
wiley   +1 more source