To view this presentation:

  • If you have an MRS account, click the Login button above.
  • New to MRS?  Create a free account here



  0       0

2014 MRS Fall Meeting


NN1.07 - Path Information Theory Methods for Coarse-Graining and Uncertainty Quantification of Molecular Systems


Dec 1, 2014 10:45am ‐ Dec 1, 2014 11:15am

Description

In this talk we discuss path-space information theory-based sensitivity analysis and parameter identification methods for complex high-dimensional dynamics, as well as information-theoretic tools for parameterized coarse-graining of non-equilibrium extended systems. Furthermore, we relate such information-theoretic methods with observables and goal-oriented approaches through the derivation of path-space Cramer-Rao-type inequalities, which also allow us to address transferability questions in coarse-graining.
The combination of proposed methodologies is capable to tackle molecular-level models with a very large number of parameters, as well as non-equilibrium processes, typically associated with coupled physicochemical mechanisms, boundary conditions, etc. (such as reaction-diffusion and/or driven systems), and where even steady states are unknown altogether, e.g. do not have a Gibbs structure. Finally, the path-wise information theory tools yield a surprisingly simple, tractable and easy-to-implement approach to quantify and rank parameter sensitivities, as well as provide reliable molecular model parameterizations based on fine-scale data through suitable path-space (dynamics-based) information criteria.

The proposed methods are tested against a wide range of high-dimensional stochastic processes, ranging from complex biochemical reaction networks with hundreds of parameters, to spatially extended Kinetic Monte Carlo models in catalysis and Langevin dynamics of interacting molecules with internal degrees of freedom.

Speaker(s):

You must be logged in and own this session in order to post comments.

Print Certificate
Review Answers
Print Transcript
Completed on: token-completed_on
Review Answers
Please select the appropriate credit type:
/
test_id: 
credits: 
completed on: 
rendered in: 
* - Indicates answer is required.
token-content

token-speaker-name
token-index
token-content
token-index
token-content
token-index
token-content
token-index
token-content
token-index
token-content
token-index
token-content
/
/
token-index
token-content
token-index
token-content