.. _model-surrogate-ensemble:

""""""""
ensemble
""""""""


Ensemble surrogates employ a collection of lower-fidelity
models to approximate a truth reference model at reduced cost.



.. toctree::
   :hidden:
   :maxdepth: 1

   model-surrogate-ensemble-ordered_model_fidelities
   model-surrogate-ensemble-truth_model_pointer


**Specification**

- *Alias:* None

- *Arguments:* None


**Child Keywords:**

+-------------------------+--------------------+------------------------------+-----------------------------------------------+
| Required/Optional       | Description of     | Dakota Keyword               | Dakota Keyword Description                    |
|                         | Group              |                              |                                               |
+=========================+====================+==============================+===============================================+
| Required (Choose One)   | Ensemble model     | `ordered_model_fidelities`__ | Specification of an hierarchy of model        |
|                         | specification      |                              | fidelities, ordered from low to high.         |
|                         |                    +------------------------------+-----------------------------------------------+
|                         |                    | `truth_model_pointer`__      | Pointer to specify a "truth" model, from      |
|                         |                    |                              | which to construct a surrogate                |
+-------------------------+--------------------+------------------------------+-----------------------------------------------+

.. __: model-surrogate-ensemble-ordered_model_fidelities.html
__ model-surrogate-ensemble-truth_model_pointer.html



**Description**


Multifidelity modeling involves the use of lower-fidelity
physics-based models as surrogates for a high-fidelity "truth"
model. These low-fidelity models can involve variations in model form,
resolution, or both.  Model form variations for reduced fidelity may
involve omitted physics or solution of approximated or averaged
physics.  Resolution variations typically involve coarsened space-time
discretizations (e.g., h- derefinement), reduced element order (e.g.,
p- derefinement), or loosened convergence tolerances.  Unlike local,
global, and multipoint surrogates, these approximations are not
data-driven (no high-fidelity data required for construction) and are
stand-alone simulation models in their own right.  As an example from
computational fluid dynamics (CFD), both model form and resolution are
varied when an inviscid, incompressible Euler model on a coarse
discretization is used as a low-fidelity surrogate for a high-fidelity
large eddy simulation (LES) model on a fine discretization.

There are two specification options for identifying the ensemble of
models.  First, the ``ordered_model_fidelities`` specification points
to a sequence of model specifications of varying fidelity, ordered
from lowest to highest fidelity.  The highest fidelity model in this
list provides the "truth" model, and each of the lower fidelity
alternatives provides different levels of approximation at different
levels of cost.  This specification defines the sequence of all model
forms, where each model specification identified in this ordered
listing can additionally identify a set of resolution controls.
Either or both of these sequences may be specified, and this is
referred to as a "multilevel hierarchy" in the case of a hierarchy of
resolutions (one entry in ``ordered_model_fidelities`` that includes
active resolution control), a "multifidelity hierarchy" in the case of
a hierarchy of model forms (multiple entries in
``ordered_model_fidelities`` without active resolution control), or a
"multilevel-multifidelity hierarchy" in the case of two-dimensional
hierarchy including both model forms and resolutions (multiple entries
in ``ordered_model_fidelities`` with one or more including active
resolution control).  Note that the multilevel-multifidelity case can
be "ragged" in the sense that not all models need to provide the same
number of (or any) resolution controls.

Second, an unordered or "non-hierarchical" ensemble of models may be
specified using ``truth_model_pointer`` in combination with
``approximation_models``, where the latter defines the set of
unordered approximations to the high-fidelity reference model.  Note
that the distinction between ordered and unordered approximations is
of little consequence within the ensemble surrogate model
implementation; rather it becomes important when aligning with the
requirements of multifidelity algorithms that either assume ordered
hierarchies or provide the flexibility to leverage general
approximation ensembles.

The ``correction`` specification identifies what type ( ``additive``,
``multiplicative``, ``combined``) and order ( ``zeroth_order``,
``first_order``, ``second_order``) of correction technique will be applied
to the low fidelity results in order to match high fidelity results
(value and potentially gradient and Hessian) at one or more points. As
described below, this is essential in the optimization context.

*Use cases*

In multifidelity surrogate-based optimization (SBO), the search
algorithm relies primarily on the lower fidelity models, which are
corrected for consistency with higher fidelity models.  The higher
fidelity models are used primarily for verifying candidate steps based
on solution of low fidelity approximate subproblems and for updating
low fidelity corrections.  In the hierarchical SBO case (as compared
to SBO with data fits), the ``correction`` specification is required,
since the omission of a correction technique would effectively
eliminate the purpose of the high fidelity model (to use a low
fidelity model without corrections, then a ``single`` model can be used
rather than a ``hierarchical`` model). Refer to
:dakkw:`model-surrogate-global` for additional information on available
correction approaches.

In multifidelity uncertainty quantification (UQ), response differences
are tracked for purposes of decomposing variance across
model/resolution levels or for constructing separate discrepancy
emulators.  In this context, correction specifications are still valid
for defining discrepancy emulation details but they are optional with
the most common cases used as defaults.



**Examples**


Theory:


