The relative entropy is fundamental to multiscale and inverse thermodynamic problems.

By M. Shell. Journal of Chemical Physics, 2008

596 citations recorded by the source corpus.

Subfields: Atmospheric Science, Atomic and Molecular Physics, and Optics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/733828a248e29a125f2eab3eea640f83eaf0221d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. ST John
2. Martin Kröger
3. Wing Kam Liu
4. Frank No'e
5. Jiang Wang
6. Ying Li
7. Gábor Csányi
8. Cecilia Clementi

In Atomic and Molecular Physics, and Optics

1. Frank No'e
2. Cecilia Clementi
3. ST John
4. Brooke E. Husic
5. Martin Kröger
6. Wing Kam Liu
7. Jiang Wang
8. Ying Li

In Statistical and Nonlinear Physics

1. Cecilia Clementi
2. Brooke E. Husic
3. Jiang Wang
4. ST John
5. Martin Kröger
6. Wing Kam Liu
7. Frank No'e
8. Ying Li

Citations in the corpus

• Challenges in Multiscale Modeling of Polymer Dynamics (2013, methodology): “The structure-based methods include the iterative Boltzmann inversion (IBI) method [82,83], the Kirkwood-Buff IBI method [164], the inverse Monte Carlo (IMC) method [165], the relative entropy method [166] and the generalized Yvon–Born–Green theory [167].”
• Many-Body Coarse-Grained Interactions Using Gaussian Approximation Potentials. (2016, methodology): “Relative Entropy Another, recent approach to structure-based coarse-graining was proposed by Shell, based on the relative entropy Srel of a CG model [103].”
• Coarse graining molecular dynamics with graph neural networks. (2020, methodology): “Another method, which we do not discuss in this report, is through relative entropy 32 , whose objective is related to that of force matching 14,33 .”