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Using higher order adjoints to accelerate the solution of UQ problems with random fields.

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Academic year: 2021

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Using higher order adjoints to accelerate the

solution of UQ problems with random fields.

Jack S. Hale, Paul Hauseux, Stéphane P. A. Bordas

Goal:

Efficient non-intrusive solution of forward UQ problems with uncertainty modelled as

Gaussian random fields.

Key features:

Local derivatives for MC variance reduction.

Second-order adjoint method (Hessians).

Data-driven low-rank model.

Efficient, scalable implementation (FEniCS, dolfin-adjoint, PETSc).

Results:

Références

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