Simulation Accuracy, Uncertainty, and Predictive Capability: A Physical Sciences Perspective

Oberkampf, William L.

doi:10.1007/978-3-319-70766-2_3

Cited by 13 publications

(6 citation statements)

References 52 publications

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“…Therefore, credibility should be a function of the available evidence of a claim, in other words: The stronger the evidence of a claim, the more credible the claim. (Oberkampf, 2019) supports the relation to truth and accuracy, as he states that simulation credibility deals with the assessment of the accuracy of certain system response quantities (SRQ) with respect to some true value or referent. He identifies three key issues on how to make credibility measurable: 1.…”

Section: Distinguishing Credibility From Qualitymentioning

confidence: 99%

Towards Continuous Simulation Credibility Assessment

Ahmann¹,

Le²,

Eichenseer³

et al. 2022

Linköping Electronic Conference Proceedings

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With the growing demand for virtual-informed decision-making in the development process of many engineering domains, the evidence in simulation results and thus simulation credibility becomes a critical aspect, in particular for releasing safety-relevant systems. However, simulation credibility is often interpreted to be of subjective nature. This paper summarizes basic assumptions for enabling the expression of credibility for building evidence in a more objective way. Based on these considerations, a concept is proposed that allows for an approximation of the credibility of simulations according to a discrete scale. The work is concluded by providing an implementation concept for a continuous simulation credibility assessment using a layered standard on top of the System Structure & Parameterization specification.

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Section: Distinguishing Credibility From Qualitymentioning

confidence: 99%

Towards Continuous Simulation Credibility Assessment

Ahmann¹,

Le²,

Eichenseer³

et al. 2022

Linköping Electronic Conference Proceedings

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show abstract

“…However, beyond this discussion she provides no criterion for a simulation not only being validated, but trustworthy. Oberkampf (2019), an aerospace engineer, discusses conditions for simulations to make reliable predictions, mostly within engineering applications. He addresses the epistemic limitations of modeling and parameter choice (see also Trucano et al (2002)) and stresses the differences between 'validation domain', inside which "several validation experiments have been conducted" (p. 89), a larger 'application domain' where one "intends to use the model from an application perspective" (ibid.…”

Section: On 'Trustworthy' Simulationsmentioning

confidence: 99%

Trustworthy simulations and their epistemic hierarchy

Mättig

2021

Synthese

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We analyze the usage of computer simulation at the LHC and derive seven jointly necessary requirements for a simulation to be considered ’trustworthy’, such that it can be used as proxy for experiments. We show that these requirements can also be applied to systems without direct experimental access and discuss their validity for properties that have not yet been probed. While being necessary, these requirements are not sufficient. Such trustworthy simulations will be analyzed for the relative epistemic statuses of simulation and material measurements, from which we argue that claims of their parity are unfounded. Instead, using credibility as a measure for epistemic status, and in view of the temporal and epistemic dependence of simulation on material measurements, we argue that the latter have a higher epistemic status than the former. We further argue that suggestions to qualify the epistemic status by ’defocussing’ on the material connection to the target system of either human or natural experiments are misleading.

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“…To reflect social structures and relevant characteristics of people’s behavior in simulations, agent-based modeling (ABM) have proven to be good complements to epidemiological differential equation models [ 9 , 17 , 23 , 24 ]. Once a model with predictive capability is established [ 25 ], it is possible to create scenarios that differ from empirical scenarios and to apply the simulation results to reality [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Modeling non-pharmaceutical interventions in the COVID-19 pandemic with survey-based simulations

Kaffai

Heiberger

2021

PLoS ONE

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Governments around the globe use non-pharmaceutical interventions (NPIs) to curb the spread of coronavirus disease 2019 (COVID-19) cases. Making decisions under uncertainty, they all face the same temporal paradox: estimating the impact of NPIs before they have been implemented. Due to the limited variance of empirical cases, researchers could so far not disentangle effects of individual NPIs or their impact on different demographic groups. In this paper, we utilize large-scale agent-based simulations in combination with Susceptible-Exposed-Infectious-Recovered (SEIR) models to investigate the spread of COVID-19 for some of the most affected federal states in Germany. In contrast to other studies, we sample agents from a representative survey. Including more realistic demographic attributes that influence agents’ behavior yields accurate predictions of COVID-19 transmissions and allows us to investigate counterfactual what-if scenarios. Results show that quarantining infected people and exploiting industry-specific home office capacities are the most effective NPIs. Disentangling education-related NPIs reveals that each considered institution (kindergarten, school, university) has rather small effects on its own, yet, that combined openings would result in large increases in COVID-19 cases. Representative survey-characteristics of agents also allow us to estimate NPIs’ effects on different age groups. For instance, re-opening schools would cause comparatively few infections among the risk-group of people older than 60 years.

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Simulation Accuracy, Uncertainty, and Predictive Capability: A Physical Sciences Perspective

Cited by 13 publications

References 52 publications

Towards Continuous Simulation Credibility Assessment

Towards Continuous Simulation Credibility Assessment

Trustworthy simulations and their epistemic hierarchy

Modeling non-pharmaceutical interventions in the COVID-19 pandemic with survey-based simulations

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