Bayesian uncertainty quantification and propagation in molecular dynamics simulations: a high performance computing framework.
By Panagiotis Angelikopoulos, Costas Papadimitriou, Petros Koumoutsakos. 2012
214 citations recorded by the source corpus.
Subfields: Materials Chemistry, Molecular Biology, Statistics and Probability
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Materials Chemistry
- Javier Llorca
- Mgd Marc Geers
- Wei Cai
- D. Papadimitriou
- S. Foiles
- Gábor Csányi
- Hongyang Cheng
- Klaus Thoeni
In Molecular Biology
- Chris J. Oates
- Nicolas Chopin
- D. Papadimitriou
- Hongyang Cheng
- Klaus Thoeni
- T. Shuku
- Mark A. Girolami
- Gábor Csányi
In Statistics and Probability
- Chris J. Oates
- Mark A. Girolami
- Nicolas Chopin
- Sharmila Karumuri
- Jitesh H. Panchal
- Costas Papadimitriou
- Rohit Tripathy
- D. Papadimitriou
Citations in the corpus
- Control functionals for Monte Carlo integration (2014, methodology): “Statistical methods are increasingly being employed to analyse complex models of physical phenomena (e.g. in climate forecasting or simulations of molecular dynamics; Slingo et al., 2009; Angelikopoulos et al., 2012).”
- Roadmap on multiscale materials modeling (2020, background): “Based on recent UQ symposia, the majority of on-going efforts address this class of problems [23].”
- An iterative Bayesian filtering framework for fast and automated calibration of DEM models (2019, methodology): “To track the evolution of the ensemble (samples and weights), the posterior expectations and variances are calculated along the loading path [25,27].”
- Probabilistic calibration of discrete element simulations using the sequential quasi-Monte Carlo filter (2018, methodology): “calibration methods for DEM or molecular dynamics simulations were recently proposed using Bayesian approaches [3,40,53].”
- Simulator-free solution of high-dimensional stochastic elliptic partial differential equations using deep neural networks (2019, methodology): “…diverse set of applications with moderate stochastic dimensionality, such as fracture mechanics [11], biological systems [12], molecular dynamics [13], nuclear engineering [14], etc. Traditional choices for surrogate models include Gaussian process regression (GPR) (or Kriging) [15, 16, 17, 18,…”
- Aerodynamic shape optimization for minimum robust drag and lift reliability constraint (2016, methodology): “For other parameters, such as the turbulence model parameters, the uncertainties can be quantified by experimental measurements using for instance Bayesian techniques [40,2,33,7,12].”