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physics.flu-dyn2024
Data-driven computation of adjoint sensitivities without adjoint solvers: An application to thermoacoustics
Defne E. Ozan, Luca Magri
Adjoint methods have been the pillar of gradient-based optimization for decades. They enable the accurate computation of a gradient (sensitivity) of a quantity of interest with res…
physics.flu-dyn2024
Hard-constrained neural networks for modelling nonlinear acoustics
Defne Ege Ozan, Luca Magri
We model acoustic dynamics in space and time from synthetic sensor data. The tasks are (i) to predict and extrapolate the spatiotemporal dynamics, and (ii) reconstruct the acoustic…