36 research outputs found
Prediction of future capacity and internal resistance of Li-ion cells from one cycle of input data
There is a large demand for models able to predict the future capacity retention and internal resistance (IR) of Lithium-ion battery cells with as little testing as possible. We provide a data-centric model accurately predicting a cell’s entire capacity and IR trajectory from one single cycle of input data. This represents a significant reduction in the amount of input data needed over previous works. Our approach characterises the capacity and IR curve through a small number of key points, which, once predicted and interpolated, describe the full curve. With this approach the remaining useful life is predicted with an 8.6% mean absolute percentage error when the input-cycle is within the first 100 cycles
Euler simulation of interacting particle systems and McKean-Vlasov SDEs with fully superlinear growth drifts in space and interaction
We consider in this work the convergence of a split-step Euler type scheme
(SSM) for the numerical simulation of interacting particle Stochastic
Differential Equation (SDE) systems and McKean-Vlasov Stochastic Differential
Equations (MV-SDEs) with full super-linear growth in the spatial and the
interaction component in the drift, and non-constant Lipschitz diffusion
coefficient.
The super-linear growth in the interaction (or measure) component stems from
convolution operations with super-linear growth functions allowing in
particular application to the granular media equation with multi-well confining
potentials. From a methodological point of view, we avoid altogether functional
inequality arguments (as we allow for non-constant non-bounded diffusion maps).
The scheme attains, in stepsize, a near-optimal classical (path-space) root
mean-square error rate of for and an optimal
rate in the non-path-space mean-square error metric. Numerical examples
illustrate all findings. In particular, the testing raises doubts if taming is
a suitable methodology for this type of problem (with convolution terms and
non-constant diffusion coefficients).Comment: 40 pages, 3 figures; Final author accepted version (to appear in IMA
J. of Num. Analysis
Forward utilities and Mean-field games under relative performance concerns
We introduce the concept of mean field games for agents using Forward
utilities of CARA type to study a family of portfolio management problems under
relative performance concerns. Under asset specialization of the fund managers,
we solve the forward-utility finite player game and the forward-utility
mean-field game. We study best response and equilibrium strategies in the
single common stock asset and the asset specialization with common noise. As an
application, we draw on the core features of the forward utility paradigm and
discuss a problem of time-consistent mean-field dynamic model selection in
sequential time-horizons.Comment: 24 page
Importance sampling for McKean-Vlasov SDEs
This paper deals with the Monte-Carlo methods for evaluating expectations of
functionals of solutions to McKean-Vlasov Stochastic Differential Equations
(MV-SDE) with drifts of super-linear growth. We assume that the MV-SDE is
approximated in the standard manner by means of an interacting particle system
and propose two importance sampling (IS) techniques to reduce the variance of
the resulting Monte Carlo estimator. In the \emph{complete measure change}
approach, the IS measure change is applied simultaneously in the coefficients
and in the expectation to be evaluated. In the \emph{decoupling} approach we
first estimate the law of the solution in a first set of simulations without
measure change and then perform a second set of simulations under the
importance sampling measure using the approximate solution law computed in the
first step.
For both approaches, we use large deviations techniques to identify an
optimisation problem for the candidate measure change. The decoupling approach
yields a far simpler optimisation problem than the complete measure change,
however, we can reduce the complexity of the complete measure change through
some symmetry arguments. We implement both algorithms for two examples coming
from the Kuramoto model from statistical physics and show that the variance of
the importance sampling schemes is up to 3 orders of magnitude smaller than
that of the standard Monte Carlo. The computational cost is approximately the
same as for standard Monte Carlo for the complete measure change and only
increases by a factor of 2--3 for the decoupled approach. We also estimate the
propagation of chaos error and find that this is dominated by the statistical
error by one order of magnitude.Comment: 29 pages, 2 Table
An iterative method for Helmholtz boundary value problems arising in wave propagation
The complex Helmholtz equation (where ) is a mainstay of computational wave
simulation. Despite its apparent simplicity, efficient numerical methods are
challenging to design and, in some applications, regarded as an open problem.
Two sources of difficulty are the large number of degrees of freedom and the
indefiniteness of the matrices arising after discretisation. Seeking to meet
them within the novel framework of probabilistic domain decomposition, we set
out to rewrite the Helmholtz equation into a form amenable to the Feynman-Kac
formula for elliptic boundary value problems. We consider two typical
scenarios, the scattering of a plane wave and the propagation inside a cavity,
and recast them as a sequence of Poisson equations. By means of stochastic
arguments, we find a sufficient and simulatable condition for the convergence
of the iterations. Upon discretisation a necessary condition for convergence
can be derived by adding up the iterates using the harmonic series for the
matrix inverse -- we illustrate the procedure in the case of finite
differences.
From a practical point of view, our results are ultimately of limited scope.
Nonetheless, this unexpected -- even paradoxical -- new direction of attack on
the Helmholtz equation proposed by this work offers a fresh perspective on this
classical and difficult problem. Our results show that there indeed exists a
predictable range in which this new ansatz works with
being far below the challenging situation.Comment: 21 pages, 6 Figures, 1 table