scholarly journals Accurate specific molecular state densities by phase space integration. I. Computational method

1992 ◽  
Vol 96 (9) ◽  
pp. 6834-6841 ◽  
Author(s):  
Michael Berblinger ◽  
Christoph Schlier
Keyword(s):  
Phase Space ◽  
Molecular State ◽  
Computational Method ◽  
Phase Space Integration ◽  
State Densities

scholarly journals Accurate specific molecular state densities by phase space integration. II. Comparison with quantum calculations on H+3and HD+2

1992 ◽  
Vol 96 (9) ◽  
pp. 6842-6849 ◽  
Author(s):  
Michael Berblinger ◽  
Christoph Schlier ◽  
Jonathan Tennyson ◽  
Steven Miller
Keyword(s):  
Phase Space ◽  
Molecular State ◽  
Quantum Calculations ◽  
Phase Space Integration ◽  
State Densities

scholarly journals THE QUAD SCHEME: A NEW METHOD FOR PHASE SPACE INTEGRATION.

1969 ◽  
Author(s):  
F.M. Mueller ◽  
J.W. Garland ◽  
M.H. Cohen ◽  
K.H. Bennemann
Keyword(s):  
Phase Space ◽  
New Method ◽  
Phase Space Integration

Phase space integration method for bound states

1997 ◽  
Vol 65 (6) ◽  
pp. 563-564 ◽  
Author(s):  
Sharada Nagabhushana ◽  
B. A. Kagali ◽  
Sivramkrishna Vijay
Keyword(s):  
Phase Space ◽  
Bound States ◽  
Integration Method ◽  
Phase Space Integration

scholarly journals Neural network-based approach to phase space integration

2020 ◽  
Vol 9 (4) ◽  
Author(s):  
Matthew Klimek ◽  
Maxim Perelstein
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Monte Carlo ◽  
Phase Space ◽  
Cross Section ◽  
Particle Physics ◽  
Probability Distributions ◽  
Excellent Performance ◽  
Phase Space Integration ◽  
Artificial Neural Network Ann

Monte Carlo methods are widely used in particle physics to integrate and sample probability distributions on phase space. We present an Artificial Neural Network (ANN) algorithm optimized for this task, and apply it to several examples of relevance for particle physics, including situations with non-trivial features such as sharp resonances and soft/collinear enhancements. Excellent performance has been demonstrated, with the trained ANN achieving unweighting efficiencies between 30% – 75%. In contrast to traditional algorithms, the ANN-based approach does not require that the phase space coordinates be aligned with resonant or other features in the cross section.

Pedestrian Covariant Phase-Space Integration

1965 ◽  
Vol 33 (12) ◽  
pp. 987-994 ◽  
Author(s):  
W. Williamson
Keyword(s):  
Phase Space ◽  
Phase Space Integration
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