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Data-driven coarse propagation to accelerate convergence

Using regression to model approximate-solution errors

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Using deep learning to overcome Komolgorov-width limitation

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This work introduces the network uncertainty quantification (NetUQ) method for performing uncertainty propagation in systems composed …

This work proposes a machine-learning framework for modeling the error incurred by approximate solutions to parameterized dynamical …

This work introduces a new method to efficiently solve optimization problems constrained by partial differential equations (PDEs) with …

Data I/O poses a significant bottleneck in large-scale CFD simulations; thus, practitioners would like to significantly reduce the …

This work proposes a data-driven method for enabling the efficient, stable time-parallel numerical solution of systems of ordinary …

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