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dc.contributor.authorCamps-Valls, Gustau
dc.date.accessioned2020-10-29T11:39:35Z
dc.date.available2020-10-29T11:39:35Z
dc.date.issued2020
dc.identifier.citationCamps-Valls, G. Advances in machine learning for modelling and understanding in earth sciences. A: . Barcelona Supercomputing Center, 2020, p. 51-52.
dc.identifier.urihttp://hdl.handle.net/2117/330962
dc.description.abstractThe Earth is a complex dynamic network system. Modelling and understanding the system is at the core of scientific endeavour. We approach these problems with machine learning algorithms. I will review several ML approaches we have developed in the last years: 1) advanced Gaussian processes models for bio-geo-physical parameter estimation, which can incorporate physical laws, blend multisensor data while providing credible confidence intervals for the estimates and improved interpretability, 2) nonlinear dimensionality reduction methods to decompose Earth data cubes in spatially-explicit and temporally-resolved modes of variability that summarize the information content of the data and allow for identifying relations with physical processes, and 3) advances in causal inference that can uncover cause and effect relations from purely observational data.
dc.format.extent2 p.
dc.languageen
dc.language.isoeng
dc.publisherBarcelona Supercomputing Center
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.subjectÀrees temàtiques de la UPC::Informàtica::Arquitectura de computadors
dc.subject.lcshHigh performance computing
dc.titleAdvances in machine learning for modelling and understanding in earth sciences
dc.typeConference report
dc.subject.lemacCàlcul intensiu (Informàtica)
dc.rights.accessOpen Access
local.citation.startingPage51
local.citation.endingPage52


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