Article

Adaptive Deep Learning for Time-Varying Systems With Hidden Parameters: Predicting Changing Input Beam Distributions of Compact Particle Accelerators

2021-04-06

Abstract excerpt

Machine learning (ML) tools are able to learn relationships between the inputs and outputs of large complex systems directly from data. However for time-varying systems, the predictive capabilities of ML tools degrade if the systems are no longer accurately represented by the data with which the ML models were trained. For complex systems, re-training is only possible if the changes are slow relative to the rate a...

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Literature Corpus work: c09bd193-0a99-5767-aadb-2bfbb4f47acc
DOI: 10.21203/rs.3.rs-373311/v1

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Adaptive Deep Learning for Time-Varying Systems With Hidden Parameters: Predicting Changing Input Beam Distributions of Compact Particle AcceleratorsDOI 10.21203/rs.3.rs-373311/v1Read

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