Feature multi-objective #292
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added 20 commits
April 14, 2016 13:30
…ective2 Conflicts: SU2_CFD/include/solver_structure.inl SU2_CFD/src/iteration_structure.cpp SU2_CFD/src/solver_direct_mean.cpp
…s (previously used whether isothermal wall existed - which caused issues if isothermal marker was not on the head node in parallel computations)
…longer needs to be the first in the list of objectives
…updated with new solution files
…p branch restart files, may update later on for more reasonable residiual values
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As we spoke about yesterday, just let me know when you've finished adding things to this PR, Heather. Code looks good to me otherwise. |
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Thanks Tom, will do. |
added 7 commits
July 30, 2016 17:49
Conflicts: Common/src/config_structure.cpp
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Aok. Looks good to me. Merging this in now before the pyWrapper PR. |
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This pull requests adds a capability for multi-objective adjoint evaluation.
In order to use this capability, a matching number of objectives and surface markers on which to evaluate those objectives, as well as weighting values for each objective must be set. This also works with the python optimization tools, where a flag (COMBINE_OBJECTIVE) determines whether the gradients will be evaluated with a single combined adjoint evaluation or with separate, sequential evaluations.
Option Syntax:
For use with shape_optimization.py:
OPT_OBJECTIVE = DRAG1.0; LIFT2.0
COMBINE_OBJECTIVE = YES
For use with continuous_adjoint, SU2_CFD, etc, or automatically set by the shape_optimization script:
OBJECTIVE = DRAG, LIFT
OBJECTIVE_WEIGHT = 1.0, -2.0
(Note: opt_objective is still effected by automatically-applied signs - that is, whatever scale is applied to lift will be *-1 to make it a maximization problem. User can now reverse that sign if desired.)
In either case, MARKER_MONITORING =( drag_surface, lift_surface)
If only one surface is specified, it will automatically be the surface for all objectives listed. Different objectives on different surfaces can also be applied, using lists of the same length.
This change required moving some scaling terms - which means that in one of the test cases the values changed because a scaling factor is moved within the boundary conditions rather than applied after the fact (the final gradient result is the same). In addition to allowing the adjoint for a weighted sum of objectives, this may theoretically allow for applying a numerically convenient scaling factor in the future - ie, in cases where the boundary terms are either too large or too small and cause numerical cancellation errors.
Currently limited to finite difference and continuous adjoint - discrete adjoint coming soon in a separate pull request.
Also coming soon: an additional test case to add this capability to the regressions.