Operator Learning: Algorithms, Analysis and Applications
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Operator Learning: Algorithms, Analysis and Applications
Approximating operators that map between function spaces can be useful for accelerating systems level tasks in scientific computing, and for discovering computational models from data. In its most basic form, learning an operator may be cast as a form of supervised learning in which the input-output pairs are functions. The talk from Cal Tech's ...
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