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PhaseShift focuses on developing interpretable machine learning models using phase space representations inspired by classical physics. It aims to enhance sample efficiency and stability in various applications, including imaging and Earth science, by creating new models that can tackle complex phenomena beyond traditi…
Science and engineering have long relied on interpretable, parsimonious models of signals and systems.
Today there is a shift to highly-parameterized neural networks trained on massive datasets.
This often makes it hard to understand what is taken from physics and what from data, and what mechanisms determine when the methods succeed or fail.
There is a need for solid foundations for scalable and interpretable scientific machine learning. A key idea is to work in phase spaces inspired by Hamilto…
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