14 research outputs found
Probabilistic Exponential Integrators
Probabilistic solvers provide a flexible and efficient framework for
simulation, uncertainty quantification, and inference in dynamical systems.
However, like standard solvers, they suffer performance penalties for certain
stiff systems, where small steps are required not for reasons of numerical
accuracy but for the sake of stability. This issue is greatly alleviated in
semi-linear problems by the probabilistic exponential integrators developed in
this paper. By including the fast, linear dynamics in the prior, we arrive at a
class of probabilistic integrators with favorable properties. Namely, they are
proven to be L-stable, and in a certain case reduce to a classic exponential
integrator -- with the added benefit of providing a probabilistic account of
the numerical error. The method is also generalized to arbitrary non-linear
systems by imposing piece-wise semi-linearity on the prior via Jacobians of the
vector field at the previous estimates, resulting in probabilistic exponential
Rosenbrock methods. We evaluate the proposed methods on multiple stiff
differential equations and demonstrate their improved stability and efficiency
over established probabilistic solvers. The present contribution thus expands
the range of problems that can be effectively tackled within probabilistic
numerics
Calibrated Adaptive Probabilistic ODE Solvers
Probabilistic solvers for ordinary differential equations assign a posterior
measure to the solution of an initial value problem. The joint covariance of
this distribution provides an estimate of the (global) approximation error. The
contraction rate of this error estimate as a function of the solver's step size
identifies it as a well-calibrated worst-case error, but its explicit numerical
value for a certain step size is not automatically a good estimate of the
explicit error. Addressing this issue, we introduce, discuss, and assess
several probabilistically motivated ways to calibrate the uncertainty estimate.
Numerical experiments demonstrate that these calibration methods interact
efficiently with adaptive step-size selection, resulting in descriptive, and
efficiently computable posteriors. We demonstrate the efficiency of the
methodology by benchmarking against the classic, widely used Dormand-Prince 4/5
Runge-Kutta method.Comment: 17 pages, 10 figures
Transforming scholarship in the archives through handwritten text recognition:Transkribus as a case study
Purpose: An overview of the current use of handwritten text recognition (HTR) on archival manuscript material, as provided by the EU H2020 funded Transkribus platform. It explains HTR, demonstrates Transkribus, gives examples of use cases, highlights the affect HTR may have on scholarship, and evidences this turning point of the advanced use of digitised heritage content. The paper aims to discuss these issues. - Design/methodology/approach: This paper adopts a case study approach, using the development and delivery of the one openly available HTR platform for manuscript material. - Findings: Transkribus has demonstrated that HTR is now a useable technology that can be employed in conjunction with mass digitisation to generate accurate transcripts of archival material. Use cases are demonstrated, and a cooperative model is suggested as a way to ensure sustainability and scaling of the platform. However, funding and resourcing issues are identified. - Research limitations/implications: The paper presents results from projects: further user studies could be undertaken involving interviews, surveys, etc. - Practical implications: Only HTR provided via Transkribus is covered: however, this is the only publicly available platform for HTR on individual collections of historical documents at time of writing and it represents the current state-of-the-art in this field. - Social implications: The increased access to information contained within historical texts has the potential to be transformational for both institutions and individuals. - Originality/value: This is the first published overview of how HTR is used by a wide archival studies community, reporting and showcasing current application of handwriting technology in the cultural heritage sector
Pick-and-mix information operators for probabilistic ODE solvers
Probabilistic numerical solvers for ordinary differential equations compute
posterior distributions over the solution of an initial value problem via
Bayesian inference. In this paper, we leverage their probabilistic formulation
to seamlessly include additional information as general likelihood terms. We
show that second-order differential equations should be directly provided to
the solver, instead of transforming the problem to first order. Additionally,
by including higher-order information or physical conservation laws in the
model, solutions become more accurate and more physically meaningful. Lastly,
we demonstrate the utility of flexible information operators by solving
differential-algebraic equations. In conclusion, the probabilistic formulation
of numerical solvers offers a flexible way to incorporate various types of
information, thus improving the resulting solutions.Comment: 13 pages, 7 figure
Fenrir: Physics-Enhanced Regression for Initial Value Problems
We show how probabilistic numerics can be used to convert an initial value
problem into a Gauss--Markov process parametrised by the dynamics of the
initial value problem. Consequently, the often difficult problem of parameter
estimation in ordinary differential equations is reduced to hyperparameter
estimation in Gauss--Markov regression, which tends to be considerably easier.
The method's relation and benefits in comparison to classical numerical
integration and gradient matching approaches is elucidated. In particular, the
method can, in contrast to gradient matching, handle partial observations, and
has certain routes for escaping local optima not available to classical
numerical integration. Experimental results demonstrate that the method is on
par or moderately better than competing approaches
Probabilistic ODE Solutions in Millions of Dimensions
Probabilistic solvers for ordinary differential equations (ODEs) have emerged
as an efficient framework for uncertainty quantification and inference on
dynamical systems. In this work, we explain the mathematical assumptions and
detailed implementation schemes behind solving {high-dimensional} ODEs with a
probabilistic numerical algorithm. This has not been possible before due to
matrix-matrix operations in each solver step, but is crucial for scientifically
relevant problems -- most importantly, the solution of discretised {partial}
differential equations. In a nutshell, efficient high-dimensional probabilistic
ODE solutions build either on independence assumptions or on Kronecker
structure in the prior model. We evaluate the resulting efficiency on a range
of problems, including the probabilistic numerical simulation of a differential
equation with millions of dimensions