Probabilistic Forecasting and Bayesian Data Assimilation

By Sebastian Reich, Colin J. Cotter. 2015

428 citations recorded by the source corpus.

Subfields: Atmospheric Science, Ocean Engineering, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/fe952df28c436b2a6151a919c3e53f57cef0342d

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In Atmospheric Science

In Ocean Engineering

In Statistical and Nonlinear Physics

Citations in the corpus

Interacting Particle Solutions of Fokker–Planck Equations Through Gradient–Log–Density Estimation (2020, background): “However, preliminary results indicate that related techniques may be applied to particle based simulations for smoothing (forward–backward) and related control problems for diffusion processes [65].”
The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting (2019, background): “This usually leads to an inverse problem (often ill-posed) that can be tackled by Bayesian parameter estimation and data assimilation (see, e.g., Reich & Cotter, 2015, for an introductory textbook on the topic).”
Data assimilation in the geosciences: An overview of methods, issues, and perspectives (2017, background): “The topic has recently reached the rank of a discipline per se, as testified by the appearance of books such as van Leeuwen, Cheng, and Reich (2015), Reich and Cotter (2015), Law, Stuart, and Zygalakis (2015), Asch, Bocquet, and Nodet (2016), or Fletcher (2017).”
Reduced basis approximation and a posteriori error bounds for 4D-Var data assimilation (2018, methodology): “…assimilation is prevalent in oceanography (Bennett 1993) and meteorology (Lynch 2015), where the dynamical system is described by partial differential equations (PDEs); see the recent texts (Law et al. 2015; Reich and Cotter 2015) and references therein for variational data assimilation in general.”
Model Reduction and Neural Networks for Parametric PDEs (2020, methodology): “on of PDEs, motivated by small-data scenarios; see the paper [12] which relates, in part, to earlier work focused on the small-data setting [7,37]. These approaches are more akin to data assimilation [45,33] where the data is incorporated into a model. 1.2. Our Contribution. The primary contribution”
Informed Machine Learning – A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems (2019, background): “Furthermore, we think that techniques from data assimilation [130] could also be helpful to combine modelling from knowledge and data.”