Results 111 to 120 of about 255,160 (310)

Cellular lethal damage of ⁶⁴Cu incorporated in mammalian genome evaluated with Monte Carlo methods. [PDF]

Front Med (Lausanne), 2023
Carrasco-Hernandez J, Ramos-Méndez J, Padilla-Rodal E, Avila-Rodriguez MA.   +3 more
europepmc   +1 more source

Multilevel Monte Carlo Methods [PDF]

, 2001
We study Monte Carlo approximations to high dimensional parameter dependent integrals. We survey the multilevel variance reduction technique introduced by the author in [4] and present extensions and new developments of it. The tools needed for the convergence analysis of vector-valued Monte Carlo methods are discussed, as well.
openaire   +1 more source

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

Comparison of calibration coefficients for a vinten ionization chamber simulated using four Monte Carlo methods. [PDF]

Appl Radiat Isot, 2023
Broder BA, Bergeron DE, Fitzgerald R, Zimmerman BE.   +3 more
europepmc   +1 more source

Adjoint Monte Carlo Method

This survey explores the development of adjoint Monte Carlo methods for solving optimization problems governed by kinetic equations, a common challenge in areas such as plasma control and device design. These optimization problems are particularly demanding due to the high dimensionality of the phase space and the randomness in evaluating the objective
Russel Caflisch, Yunan Yang
openaire   +2 more sources

Speculative moves : multithreading Markov Chain Monte Carlo programs [PDF]

, 2008
The increasing availability of multi-core and multi-processor architectures provides new opportunities for improving the performance of many computer simulations.
Bhalerao, Abhir, Byrd, Jonathan M. R., Jarvis, Stephen A.   +2 more
core  

Schooling Trajectories and the Development of Brain Dynamics: A Comparative Study of Montessori and Traditional Education

Advanced Science, EarlyView.
We investigate whether Montessori and traditional schooling systems shape the developmental trajectory of large‐scale brain dynamics in different ways. We quantify the arrow of time (“non‐reversibility”) in neural activity during resting state and movie‐watching, revealing distinct maturational patterns.
Elvira del Agua, Anira Escrichs, Yonatan Sanz Perl, Morten L. Kringelbach, Adele Diamond, Solange Denervaud, Gustavo Deco   +6 more
wiley   +1 more source

Machine learning meets Monte Carlo methods for models of muscle's molecular machinery to classify mutations. [PDF]

J Gen Physiol, 2023
Asencio A, Malingen S, Kooiker KB, Powers JD, Davis J, Daniel T, Moussavi-Harami F.   +6 more
europepmc   +1 more source

Regression-Based Methods for Using Control and Antithetic Variates in Monte Carlo Experiments [PDF]

Methods based on linear regression provide a very easy way to use the information in control and antithetic variates to improve the efficiency with which certain features of the distributions of estimators and test statistics are estimated in Monte Carlo
Russell Davidson, James G. Mackinnon
core  

Deciphering Short‐Range Order in 2D Transition Metal Dichalcogenides: From Origin to Multi‐Scale Property Modulation

Advanced Science, EarlyView.
Short‐range order in 2D transition metal dichalcogenides is revealed as a new design paradigm. Driven by chemical affinity and atomic size, it governs properties across scales. Weak ordering tunes site‐resolved magnetism and d‐band centers, while strong ordering eliminates gap states to open band gaps.
Hanyu Liu, Linggang Zhu, Jian Zhou, Zhimei Sun   +3 more
wiley   +1 more source