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Thesis

Physics-informed machine learning: from concepts to real-world applications

Abstract:

Machine learning (ML) has caused a fundamental shift in how we practice science, with many now placing learning from data at the focal point of their research. As the complexity of the scientific problems we want to study increases, and the amount of data generated by today's scientific experiments grows, ML is helping to automate, accelerate and enhance traditional workflows.

Emerging at the forefront of this revolution is a field called scientific machine learning (SciML). The ce...

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Division:
MPLS
Department:
Computer Science
Role:
Author

Contributors

Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Supervisor
Institution:
University of Oxford
Division:
MPLS
Department:
Earth Sciences
Role:
Supervisor
ORCID:
0000-0002-9051-1060
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Programme:
EPSRC Centre for Doctoral Training in Autonomous Intelligent Machines and Systems (AIMS)
Grant:
EP/L015897/1
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford

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