Dimensionality reduction of complex reaction networks in heterogeneous catalysis: From l inear‐scaling relationships to statistical learning techniques

2021 ◽  
Author(s):  
Sergio Pablo‐García ◽  
Rodrigo García‐Muelas ◽  
Albert Sabadell‐Rendón ◽  
Núria López
Keyword(s):  
Heterogeneous Catalysis ◽  
Dimensionality Reduction ◽  
Statistical Learning ◽  
Reaction Networks ◽  
Complex Reaction ◽  
Scaling Relationships ◽  
Learning Techniques ◽  
Statistical Learning Techniques

scholarly journals Multiscale and Innovative Kinetic Approaches in Heterogeneous Catalysis

Catalysts ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 501
Author(s):  
Yves Schuurman ◽  
Pascal Granger
Keyword(s):  
Heterogeneous Catalysis ◽  
Catalytic Reactions ◽  
Reaction Networks ◽  
Complex Reaction ◽  
Reactor Modeling ◽  
Heterogeneous Catalytic

Kinetics and reactor modeling for heterogeneous catalytic reactions are prominent tools for investigating, and understanding, the catalyst functionalities at nanoscale, and related rates of complex reaction networks [...]

scholarly journals Reply

2005 ◽  
Vol 133 (12) ◽  
pp. 3724-3729 ◽  
Author(s):  
Vladimir M. Krasnopolsky ◽  
Michael S. Fox-Rabinovitz ◽  
Dmitry V. Chalikov
Keyword(s):  
Statistical Learning ◽  
Numerical Models ◽  
Interdisciplinary Field ◽  
Learning Techniques ◽  
Statistical Learning Techniques ◽  
Speed Up ◽  
Network Application ◽  
Similarities And Differences ◽  
Methodological Aspects ◽  
Modern Machine

Abstract This reply is aimed at clarifying and further discussing the methodological aspects of this neural network application for a better understanding of the technique by the journal readership. The similarities and differences of two approaches and their areas of application are discussed. These two approaches outline a new interdisciplinary field based on application of neural networks (and probably other modern machine or statistical learning techniques) to significantly speed up calculations of time-consuming components of atmospheric and oceanic numerical models.

scholarly journals Stochastic earned value analysis using Monte Carlo simulation and statistical learning techniques

2015 ◽  
Vol 33 (7) ◽  
pp. 1597-1609 ◽  
Author(s):  
Fernando Acebes ◽  
María Pereda ◽  
David Poza ◽  
Javier Pajares ◽  
José Manuel Galán
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Statistical Learning ◽  
Earned Value ◽  
Value Analysis ◽  
Earned Value Analysis ◽  
Learning Techniques ◽  
Statistical Learning Techniques

scholarly journals Force Field Parametrization of Metal Ions from Statistical Learning Techniques

2017 ◽  
Vol 14 (1) ◽  
pp. 255-273 ◽  
Author(s):  
Francesco Fracchia ◽  
Gianluca Del Frate ◽  
Giordano Mancini ◽  
Walter Rocchia ◽  
Vincenzo Barone
Keyword(s):  
Force Field ◽  
Metal Ions ◽  
Statistical Learning ◽  
Learning Techniques ◽  
Statistical Learning Techniques ◽  
Force Field Parametrization

Characterizing and detecting climate signals in observations and models using statistical learning

2020 ◽  
Author(s):  
Sebastian Sippel ◽  
Nicolai Meinshausen ◽  
Erich Fischer ◽  
Eniko Szekely ◽  
Reto Knutti
Keyword(s):  
Statistical Learning ◽  
Spatial Patterns ◽  
Climate Model ◽  
Learning Algorithm ◽  
Global Climate ◽  
Climatic Changes ◽  
Learning Techniques ◽  
Statistical Learning Techniques ◽  
Climate Signals ◽  
Global Mean

<p>Internal atmospheric variability fundamentally limits short- and medium-term climate predictability and obscures evidence of climatic changes on regional scales. We discuss the suitability of incorporating statistical learning techniques to detect global climate signals from spatial patterns.</p><p>Our detection approach uses climate model simulations and a statistical learning algorithm to encapsulate the relationship between spatial patterns of daily temperature and humidity, and key climate change metrics such as annual global mean temperature or Earth’s energy imbalance. Observations are then projected onto this relationship to detect climatic changes. We show that fingerprints of changes in climate can be assessed and detected in the observed global climate record at time steps such as months or days by comparison against a historical baseline from CMIP5 simulations or reanalyses. Detection can be achieved also when ignoring the long-term global mean warming trend.</p><p>We further discuss how these approaches could be extended by using statistical techniques that would work well under variations of specific external forcings, e.g. solar or volcanic forcing, to predict only variations in a specific external forcing. Overall, we conclude that statistical learning techniques that characterize multivariate signals from high-dimensional climate data are a useful tool for the detection of climate signals at regional and global scales.</p>

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