Boon and Bane: On the Role of Adjustable Parameters in Simulation Models

Hasse, Hans; Lenhard, Johannes

doi:10.1007/978-3-319-54469-4_6

Cited by 33 publications

(15 citation statements)

References 27 publications

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“…As Brown's characteristic curves are directly related to virial coefficients, also the second and third virial coefficient are studied. This comparison is of particular interest, since the virial coefficients of the LJ fluid can be computed exactly from their definitions in statistical mechanics, while reference data obtained from computer simulations are subject to errors and uncertainties [7,71,72]. Brown's characteristic curves and the virial coefficients are directly linked in the limit of the ideal gas and therefore corporately investigated in the present work.…”

Section: Introductionmentioning

confidence: 99%

Characteristic Curves of the Lennard-Jones Fluid

Stephan

Deiters

2020

Int J Thermophys

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Equations of state based on intermolecular potentials are often developed about the Lennard-Jones (LJ) potential. Many of such EOS have been proposed in the past. In this work, 20 LJ EOS were examined regarding their performance on Brown's characteristic curves and characteristic state points. Brown's characteristic curves are directly related to the virial coefficients at specific state points, which can be computed exactly from the intermolecular potential. Therefore, also the second and third virial coefficient of the LJ fluid were investigated. This approach allows a comparison of available LJ EOS at extreme conditions. Physically based, empirical, and semi-theoretical LJ EOS were examined. Most investigated LJ EOS exhibit some unphysical artifacts.

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Section: Introductionmentioning

confidence: 99%

Characteristic Curves of the Lennard-Jones Fluid

Stephan

Deiters

2020

Int J Thermophys

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“…Analyses of the modeling steps involved in devising a CS of this general type are now fairly common, and found, in varying detail and explicitness, in e.g. Winsberg (1999Winsberg ( , 2010, Humphreys (2004), Beisbart (2012), Hasse and Lenhard (2017), Boge (2019b), or Durán (2020).…”

Section: Opacity: From Computer Simulations To Deep Learningmentioning

confidence: 99%

Two Dimensions of Opacity and the Deep Learning Predicament

Boge

2021

Minds & Machines

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Deep neural networks (DNNs) have become increasingly successful in applications from biology to cosmology to social science. Trained DNNs, moreover, correspond to models that ideally allow the prediction of new phenomena. Building in part on the literature on ‘eXplainable AI’ (XAI), I here argue that these models are instrumental in a sense that makes them non-explanatory, and that their automated generation is opaque in a unique way. This combination implies the possibility of an unprecedented gap between discovery and explanation: When unsupervised models are successfully used in exploratory contexts, scientists face a whole new challenge in forming the concepts required for understanding underlying mechanisms.

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“…2 Many philosophers have engaged with the problem of epistemic opacity and suggested reliabilism as the most suitable solution, although most of them have not provided a full fledged account. A shortlist includes [23], [17] and [21], among others. The exception is [9], who offers an early attempt to reconstruct reliabilism in the context of computer simulations.…”

Section: Introductionmentioning

confidence: 99%

Grounds for Trust: Essential Epistemic Opacity and Computational Reliabilism

Durán

Formanek

2018

Minds & Machines

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Several philosophical issues in connection with computer simulations rely on the assumption that results of simulations are trustworthy. Examples of these include the debate on the experimental role of computer simulations [35,28], the nature of computer data [3,20], and the explanatory power of computer simulations [22,10]. The aim of this article is to show that these authors are right in assuming that results of computer simulations are to be trusted when computer simulations are reliable processes. After a short reconstruction of the problem of epistemic opacity, the article elaborates extensively on computational reliabilism, a specified form of process reliabilism with computer simulations located at the center. The article ends with a discussion of four sources for computational reliabilism, namely, verification and validation, robustness analysis for computer simulations, a history of (un)successful implementations, and the role of expert knowledge in simulations.

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Boon and Bane: On the Role of Adjustable Parameters in Simulation Models

Cited by 33 publications

References 27 publications

Characteristic Curves of the Lennard-Jones Fluid

Characteristic Curves of the Lennard-Jones Fluid

Two Dimensions of Opacity and the Deep Learning Predicament

Grounds for Trust: Essential Epistemic Opacity and Computational Reliabilism

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