Lithium‐ion batteries (LIBs) are vital energy‐storage devices in modern society. However,
the performance and cost are still not satisfactory in terms of energy density, power density …

We deliver a call to arms for probabilistic numerical methods: algorithms for numerical tasks,
including linear algebra, integration, optimization and solving differential equations, that …

In this study, we utilize a density functional theory-machine learning framework to develop a
high-throughput screening method for designing new molecular electrode materials. For this …

Over forty years ago average-case error was proposed in the applied mathematics literature
as an alternative criterion with which to assess numerical methods. In contrast to worst-case …

Imbalanced datasets pose significant challenges in areas including neuroscience, cognitive
science, and medical diagnostics, where accurately detecting minority classes is essential …

Supplement to “Asymptotic and finite-sample properties of estimators based on stochastic
gradients”. The proofs of all technical results are provided in an online supplement [Toulis …

We consider a modification of the covariance function in Gaussian processes to correctly
account for known linear constraints. By modelling the target function as a transformation of …

We propose an active learning method for discovering low-dimensional structure in high-
dimensional Gaussian process (GP) tasks. Such problems are increasingly frequent and …

Runge-Kutta methods are the classic family of solvers for ordinary differential equations
(ODEs), and the basis for the state of the art. Like most numerical methods, they return point …

This article attempts to place the emergence of probabilistic numerics as a mathematical–
statistical research field within its historical context and to explore how its gradual …