dc.date.accessioned | 2023-07-24T13:45:36Z | |
dc.date.available | 2023-07-24T13:45:36Z | |
dc.date.issued | 2023 | |
dc.identifier.uri | http://publications.mfo.de/handle/mfo/4057 | |
dc.description.abstract | Rapid progress in machine learning is enabling scientific advances across a range of disciplines. However, the utility of machine learning for science remains constrained by its current inability to translate insights from data about the dynamics of a system to new scientific knowledge about why those dynamics emerge, as traditionally represented by physical modelling. Mathematics is the interface that bridges data-driven and physical models of the world and can provide a foundation for delivering such knowledge. This workshop convened researchers working across domains with a shared interest in mathematics, machine learning, and their application in the sciences, to explore how tools of mathematics can help build machine learning tools for scientific discovery. | |
dc.title | Machine Learning for Science: Mathematics at the Interface of Data-driven and Mechanistic Modelling | |
dc.rights.license | Dieses Dokument darf im Rahmen von § 53 UrhG zum eigenen Gebrauch kostenfrei heruntergeladen, gelesen, gespeichert und ausgedruckt, aber nicht im Internet bereitgestellt oder an Außenstehende weitergegeben werden. | de |
dc.rights.license | This document may be downloaded, read, stored and printed for your own use within the limits of § 53 UrhG but it may not be distributed via the internet or passed on to external parties. | en |
dc.identifier.doi | 10.14760/OWR-2023-26 | |
local.series.id | OWR-2023-26 | |
local.subject.msc | 60 | |
local.subject.msc | 62 | |
local.subject.msc | 68 | |
local.subject.msc | 85 | |
local.subject.msc | 86 | |
local.subject.msc | 92 | |
local.date-range | 11 Jun - 16 Jun 2023 | |
local.workshopcode | 2324 | |
local.workshoptitle | Machine Learning for Science: Mathematics at the Interface of Data-driven and Mechanistic Modelling | |
local.organizers | Neil Lawrence, Cambridge UK; Jessica Montgomery, Cambridge UK; Bernhard Schölkopf, Tübingen | |
local.report-name | Workshop Report 2023,26 | |
local.opc-photo-id | 2324 | |
local.publishers-doi | 10.4171/OWR/2023/26 | |