21 research outputs found
Efficient Localization of Discontinuities in Complex Computational Simulations
Surrogate models for computational simulations are input-output
approximations that allow computationally intensive analyses, such as
uncertainty propagation and inference, to be performed efficiently. When a
simulation output does not depend smoothly on its inputs, the error and
convergence rate of many approximation methods deteriorate substantially. This
paper details a method for efficiently localizing discontinuities in the input
parameter domain, so that the model output can be approximated as a piecewise
smooth function. The approach comprises an initialization phase, which uses
polynomial annihilation to assign function values to different regions and thus
seed an automated labeling procedure, followed by a refinement phase that
adaptively updates a kernel support vector machine representation of the
separating surface via active learning. The overall approach avoids structured
grids and exploits any available simplicity in the geometry of the separating
surface, thus reducing the number of model evaluations required to localize the
discontinuity. The method is illustrated on examples of up to eleven
dimensions, including algebraic models and ODE/PDE systems, and demonstrates
improved scaling and efficiency over other discontinuity localization
approaches
A continuous analogue of the tensor-train decomposition
We develop new approximation algorithms and data structures for representing
and computing with multivariate functions using the functional tensor-train
(FT), a continuous extension of the tensor-train (TT) decomposition. The FT
represents functions using a tensor-train ansatz by replacing the
three-dimensional TT cores with univariate matrix-valued functions. The main
contribution of this paper is a framework to compute the FT that employs
adaptive approximations of univariate fibers, and that is not tied to any
tensorized discretization. The algorithm can be coupled with any univariate
linear or nonlinear approximation procedure. We demonstrate that this approach
can generate multivariate function approximations that are several orders of
magnitude more accurate, for the same cost, than those based on the
conventional approach of compressing the coefficient tensor of a tensor-product
basis. Our approach is in the spirit of other continuous computation packages
such as Chebfun, and yields an algorithm which requires the computation of
"continuous" matrix factorizations such as the LU and QR decompositions of
vector-valued functions. To support these developments, we describe continuous
versions of an approximate maximum-volume cross approximation algorithm and of
a rounding algorithm that re-approximates an FT by one of lower ranks. We
demonstrate that our technique improves accuracy and robustness, compared to TT
and quantics-TT approaches with fixed parameterizations, of high-dimensional
integration, differentiation, and approximation of functions with local
features such as discontinuities and other nonlinearities
Bayesian Identification of Nonseparable Hamiltonian Systems Using Stochastic Dynamic Models
This paper proposes a probabilistic Bayesian formulation for system
identification (ID) and estimation of nonseparable Hamiltonian systems using
stochastic dynamic models. Nonseparable Hamiltonian systems arise in models
from diverse science and engineering applications such as astrophysics,
robotics, vortex dynamics, charged particle dynamics, and quantum mechanics.
The numerical experiments demonstrate that the proposed method recovers
dynamical systems with higher accuracy and reduced predictive uncertainty
compared to state-of-the-art approaches. The results further show that accurate
predictions far outside the training time interval in the presence of sparse
and noisy measurements are possible, which lends robustness and
generalizability to the proposed approach. A quantitative benefit is prediction
accuracy with less than 10% relative error for more than 12 times longer than a
comparable least-squares-based method on a benchmark problem
An Incremental Tensor Train Decomposition Algorithm
We present a new algorithm for incrementally updating the tensor-train
decomposition of a stream of tensor data. This new algorithm, called the
tensor-train incremental core expansion (TT-ICE) improves upon the current
state-of-the-art algorithms for compressing in tensor-train format by
developing a new adaptive approach that incurs significantly slower rank growth
and guarantees compression accuracy. This capability is achieved by limiting
the number of new vectors appended to the TT-cores of an existing accumulation
tensor after each data increment. These vectors represent directions orthogonal
to the span of existing cores and are limited to those needed to represent a
newly arrived tensor to a target accuracy. We provide two versions of the
algorithm: TT-ICE and TT-ICE accelerated with heuristics (TT-ICE). We
provide a proof of correctness for TT-ICE and empirically demonstrate the
performance of the algorithms in compressing large-scale video and scientific
simulation datasets. Compared to existing approaches that also use rank
adaptation, TT-ICE achieves 57 higher compression and up to 95%
reduction in computational time.Comment: 22 pages, 7 figures, for the python code of TT-ICE and TT-ICE
algorithms see https://github.com/dorukaks/TT-IC