12,080 research outputs found
Greedy kernel methods for accelerating implicit integrators for parametric ODEs
We present a novel acceleration method for the solution of parametric ODEs by
single-step implicit solvers by means of greedy kernel-based surrogate models.
In an offline phase, a set of trajectories is precomputed with a high-accuracy
ODE solver for a selected set of parameter samples, and used to train a kernel
model which predicts the next point in the trajectory as a function of the last
one. This model is cheap to evaluate, and it is used in an online phase for new
parameter samples to provide a good initialization point for the nonlinear
solver of the implicit integrator. The accuracy of the surrogate reflects into
a reduction of the number of iterations until convergence of the solver, thus
providing an overall speedup of the full simulation. Interestingly, in addition
to providing an acceleration, the accuracy of the solution is maintained, since
the ODE solver is still used to guarantee the required precision. Although the
method can be applied to a large variety of solvers and different ODEs, we will
present in details its use with the Implicit Euler method for the solution of
the Burgers equation, which results to be a meaningful test case to demonstrate
the method's features
The ROMES method for statistical modeling of reduced-order-model error
This work presents a technique for statistically modeling errors introduced
by reduced-order models. The method employs Gaussian-process regression to
construct a mapping from a small number of computationally inexpensive `error
indicators' to a distribution over the true error. The variance of this
distribution can be interpreted as the (epistemic) uncertainty introduced by
the reduced-order model. To model normed errors, the method employs existing
rigorous error bounds and residual norms as indicators; numerical experiments
show that the method leads to a near-optimal expected effectivity in contrast
to typical error bounds. To model errors in general outputs, the method uses
dual-weighted residuals---which are amenable to uncertainty control---as
indicators. Experiments illustrate that correcting the reduced-order-model
output with this surrogate can improve prediction accuracy by an order of
magnitude; this contrasts with existing `multifidelity correction' approaches,
which often fail for reduced-order models and suffer from the curse of
dimensionality. The proposed error surrogates also lead to a notion of
`probabilistic rigor', i.e., the surrogate bounds the error with specified
probability
Linear Time Feature Selection for Regularized Least-Squares
We propose a novel algorithm for greedy forward feature selection for
regularized least-squares (RLS) regression and classification, also known as
the least-squares support vector machine or ridge regression. The algorithm,
which we call greedy RLS, starts from the empty feature set, and on each
iteration adds the feature whose addition provides the best leave-one-out
cross-validation performance. Our method is considerably faster than the
previously proposed ones, since its time complexity is linear in the number of
training examples, the number of features in the original data set, and the
desired size of the set of selected features. Therefore, as a side effect we
obtain a new training algorithm for learning sparse linear RLS predictors which
can be used for large scale learning. This speed is possible due to matrix
calculus based short-cuts for leave-one-out and feature addition. We
experimentally demonstrate the scalability of our algorithm and its ability to
find good quality feature sets.Comment: 17 pages, 15 figure
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