When we can trust computers (and when we can't)

Abstract

With the relentless rise of computer power, there is a widespread expectation that computers can solve the most pressing problems of science, and even more besides. We explore the limits of computational modelling and conclude that, in the domains of science and engineering which are relatively simple and firmly grounded in theory, these methods are indeed powerful. Even so, the availability of code, data and documentation, along with a range of techniques for validation, verification and uncertainty quantification, are essential for building trust in computer-generated findings. When it comes to complex systems in domains of science that are less firmly grounded in theory, notably biology and medicine, to say nothing of the social sciences and humanities, computers can create the illusion of objectivity, not least because the rise of big data and machine-learning pose new challenges to reproducibility, while lacking true explanatory power. We also discuss important aspects of the natural world which cannot be solved by digital means. In the long term, renewed emphasis on analogue methods will be necessary to temper the excessive faith currently placed in digital computation. This article is part of the theme issue ‘Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico ’.

Uncertainty quantification in classical molecular dynamics

“…We do not provide a comprehensive review of molecular dynamics applications. We shall spend much less time looking at verification and validation, which are respectively concerned with whether molecular dynamics computer programs are solving the correct equations and how well their output agrees with experimental observations [ 4 ].…”

Using a Digital Twin of an Electrical Stimulation Device to Monitor and Control the Electrical Stimulation of Cells in vitro

“…Numerical models are often approached with scepticism regarding their validity. Particularly in biosciences, verification, validation and uncertainty quantification (VVUQ) become highly important ( Coveney and Highfield, 2021 ). In the context of electrical stimulation, the need for thorough VVUQ is corroborated by problems in the reproducibility of experimental studies, which have been identified in recent research ( Portelli et al, 2018 ; Budde et al, 2019 ; Guette-Marquet et al, 2021 ).…”

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