Dynamics of molecules with internal degrees of freedom by multiple time‐step methods

1993 ◽  
Vol 99 (10) ◽  
pp. 8063-8074 ◽  
Author(s):  
Masakatsu Watanabe ◽  
Martin Karplus
Keyword(s):  
Degrees Of Freedom ◽  
Multiple Time ◽  
Time Step ◽  
Multiple Time Step ◽  
Internal Degrees Of Freedom

scholarly journals Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3517 ◽  
Author(s):  
Anh Ngoc-Lan Huynh ◽  
Ravinesh C. Deo ◽  
Duc-Anh An-Vo ◽  
Mumtaz Ali ◽  
Nawin Raj ◽  
...  
Keyword(s):  
Deep Learning ◽  
Solar Radiation ◽  
Short Term Memory ◽  
Global Solar Radiation ◽  
Multiple Time ◽  
Short Term ◽  
Time Step ◽  
Term Memory ◽  
Multiple Time Step ◽  
Long Short Term Memory

This paper aims to develop the long short-term memory (LSTM) network modelling strategy based on deep learning principles, tailored for the very short-term, near-real-time global solar radiation (GSR) forecasting. To build the prescribed LSTM model, the partial autocorrelation function is applied to the high resolution, 1 min scaled solar radiation dataset that generates statistically significant lagged predictor variables describing the antecedent behaviour of GSR. The LSTM algorithm is adopted to capture the short- and the long-term dependencies within the GSR data series patterns to accurately predict the future GSR at 1, 5, 10, 15, and 30 min forecasting horizons. This objective model is benchmarked at a solar energy resource rich study site (Bac-Ninh, Vietnam) against the competing counterpart methods employing other deep learning, a statistical model, a single hidden layer and a machine learning-based model. The LSTM model generates satisfactory predictions at multiple-time step horizons, achieving a correlation coefficient exceeding 0.90, outperforming all of the counterparts. In accordance with robust statistical metrics and visual analysis of all tested data, the study ascertains the practicality of the proposed LSTM approach to generate reliable GSR forecasts. The Diebold–Mariano statistic test also shows LSTM outperforms the counterparts in most cases. The study confirms the practical utility of LSTM in renewable energy studies, and broadly in energy-monitoring devices tailored for other energy variables (e.g., hydro and wind energy).

Dangers of Multiple Time Step Methods

1993 ◽  
Vol 109 (2) ◽  
pp. 318-328 ◽  
Author(s):  
Jeffrey J. Biesiadecki ◽  
Robert D. Skeel
Keyword(s):  
Multiple Time ◽  
Time Step ◽  
Multiple Time Step

Multiple time-step methods in molecular dynamics

1978 ◽  
Vol 35 (3) ◽  
pp. 639-648 ◽  
Author(s):  
W.B. Streett ◽  
D.J. Tildesley ◽  
G. Saville
Keyword(s):  
Molecular Dynamics ◽  
Multiple Time ◽  
Time Step ◽  
Multiple Time Step

scholarly journals Multiple time step integrators in ab initio molecular dynamics

2014 ◽  
Vol 140 (8) ◽  
pp. 084116 ◽  
Author(s):  
Nathan Luehr ◽  
Thomas E. Markland ◽  
Todd J. Martínez
Keyword(s):  
Molecular Dynamics ◽  
Ab Initio ◽  
Ab Initio Molecular Dynamics ◽  
Multiple Time ◽  
Time Step ◽  
Multiple Time Step

Multiple Time Step Brownian Dynamics for Long Time Simulation of Biomolecules

2003 ◽  
Vol 29 (8) ◽  
pp. 471-478 ◽  
Author(s):  
Tadashi Ando ◽  
Toshiyuki Meguro ◽  
Ichiro Yamato
Keyword(s):  
Brownian Dynamics ◽  
Multiple Time ◽  
Time Step ◽  
Multiple Time Step ◽  
Time Simulation ◽  
Long Time

On Multiple Time-step Algorithms and the Ewald Sum

1994 ◽  
Vol 13 (3) ◽  
pp. 195-204 ◽  
Author(s):  
T. Forester ◽  
W. Smith
Keyword(s):  
Multiple Time ◽  
Time Step ◽  
Ewald Sum ◽  
Multiple Time Step
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