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The contemporary focus on high fidelity implementation of research-based practices often creates tensions for educators who seek to balance fidelity with needed flexibility as they strive to improve learner outcomes. In an effort to improve how decisions are made such that flexibility is achieved while fidelity to core components is maintained, this chapter begins with a discussion of the role of fidelity in research and practice. Particular attention is given to current conceptualizations of fidelity that may help inform theoretically and empirically driven adaptations to research-based practices. Specifically, we describe adaptations based on the instructional context for implementation and the characteristics of the individual learners. A framework for adapting research-based practices is then presented with relevant examples from research designed to optimize learner responsiveness without sacrificing fidelity to core components. The chapter ends with implications and future directions for research and practice.

Current methods for flow field reconstruction mainly rely on data-driven algorithms which require an immense amount of experimental or field-measured data. Physics-informed neural network (PINN), which was proposed to encode physical laws into neural networks, is a less data-demanding approach for flow field reconstruction. However, when the fluid physics is complex, it is tricky to obtain accurate solutions under the PINN framework. This study aims to propose a physics-based data-driven approach for time-averaged flow field reconstruction which can overcome the hurdles of the above methods.

A multifidelity strategy leveraging PINN and a nonlinear information fusion (NIF) algorithm is proposed. Plentiful low-fidelity data are generated from the predictions of a PINN which is constructed purely using Reynold-averaged Navier–Stokes equations, while sparse high-fidelity data are obtained by field or experimental measurements. The NIF algorithm is performed to elicit a multifidelity model, which blends the nonlinear cross-correlation information between low- and high-fidelity data.

Two experimental cases are used to verify the capability and efficacy of the proposed strategy through comparison with other widely used strategies. It is revealed that the missing flow information within the whole computational domain can be favorably recovered by the proposed multifidelity strategy with use of sparse measurement/experimental data. The elicited multifidelity model inherits the underlying physics inherent in low-fidelity PINN predictions and rectifies the low-fidelity predictions over the whole computational domain. The proposed strategy is much superior to other contrastive strategies in terms of the accuracy of reconstruction.

In this study, a physics-informed data-driven strategy for time-averaged flow field reconstruction is proposed which extends the applicability of the PINN framework. In addition, embedding physical laws when training the multifidelity model leads to less data demand for model development compared to purely data-driven methods for flow field reconstruction.

The purpose of this study is to describe the development of the 14-item reflective fostering fidelity rating (RFFR), an observational rating system to evaluate model fidelity of group facilitators in the Reflective Fostering Programme (RFP), a mentalisation-based psychoeducation programme to support foster carers. The authors assess usability, dimensionality, inter-rater reliability and discriminative ability of the RFFR.

Eighty video clip extracts documenting 20 RFP sessions were independently rated by four raters using the RFFR. The dimensionality of the RFFR was assessed using principal components analysis. Inter-rater agreement was assessed using the intra-class correlation coefficient.

The proportion of missing ratings was low at 2.8%. A single principal component summarised over 90% of the variation in ratings for each rater. The inter-rater reliability of individual item ratings was poor-to-moderate, but a summary score had acceptable inter-rater reliability. The authors present evidence that the RFFR can distinguish RFP sessions that differ in treatment fidelity.

To the best of the authors’ knowledge, this is the first investigation and report of the RFFR’s validity in assessing the programme fidelity of the RFP. The paper concludes that the RFFR is an appropriate rating measure for treatment fidelity of the RFP and useful for the purposes of both quality control and supervision.

The purpose of this work is to apply and compare surrogate-assisted and multi-fidelity, multi-objective optimization (MOO) algorithms to simulation-based aerodynamic design exploration.

The three algorithms for multi-objective aerodynamic optimization compared in this work are the combination of evolutionary algorithms, design space reduction and surrogate models, the multi-fidelity point-by-point Pareto set identification and the multi-fidelity sequential domain patching (SDP) Pareto set identification. The algorithms are applied to three cases, namely, an analytical test case, the design of transonic airfoil shapes and the design of subsonic wing shapes, and are evaluated based on the resulting best possible trade-offs and the computational overhead.

The results show that all three algorithms yield comparable best possible trade-offs for all the test cases. For the aerodynamic test cases, the multi-fidelity Pareto set identification algorithms outperform the surrogate-assisted evolutionary algorithm by up to 50 per cent in terms of cost. Furthermore, the point-by-point algorithm is around 27 per cent more efficient than the SDP algorithm.

The novelty of this work includes the first applications of the SDP algorithm to multi-fidelity aerodynamic design exploration, the first comparison of these multi-fidelity MOO algorithms and new results of a complex simulation-based multi-objective aerodynamic design of subsonic wing shapes involving two conflicting criteria, several nonlinear constraints and over ten design variables.

The purpose of this paper is to explore the predictive validity of two measures of fidelity to the individual placement and support (IPS) approach to supported employment.

A systematic review and meta-analysis was conducted of IPS programs. In total, 30 studies provided information characterizing 69 cohorts and 8,392 participants. Predictive validity was assessed by a precision and negative prediction analysis and by multivariate analysis of deviance.

Fidelity scores on the IPS-15 scale of 60 or less accurately predicted poor outcomes, defined as 43 percent or less of participants commencing employment, in 100 percent of cohorts. Among cohorts with IPS-15 fidelity scores of 61-75, 63 percent attained good employment outcomes defined as 44 percent or more commencing employment. A similar pattern emerged from the precision analysis of the smaller sample of IPS-25 cohorts. Multivariate analysis of deviance for studies using the IPS-15 scale examined six cohort characteristics. Following adjustment for fidelity score, only fidelity score (χ²=15.31, df=1, p<0.001) and author group (χ²=35.01, df=17, p=0.01) representing an aspect of cohort heterogeneity, remained associated with commencing employment.

This study provides evidence of moderate, yet important, predictive validity of the IPS-15 scale across diverse international and research contexts. The smaller sample of IPS-25 studies limited the analysis that could be conducted.

Program implementation leaders are encouraged to first focus on attaining good fidelity, then supplement fidelity monitoring with tracking the percentage of new clients who obtain a competitive job employment over a pre-defined period of time.

The evidence indicates that good fidelity may be necessary but not sufficient for good competitive employment outcomes.

The purpose of this paper is to reduce the overall computational time of aerodynamic shape optimization that involves accurate high-fidelity simulation models.

The proposed approach is based on the surrogate-based optimization paradigm. In particular, multi-fidelity surrogate models are used in the optimization process in place of the computationally expensive high-fidelity model. The multi-fidelity surrogate is constructed using physics-based low-fidelity models and a proper correction. This work introduces a novel correction methodology – referred to as the adaptive response prediction (ARP). The ARP technique corrects the low-fidelity model response, represented by the airfoil pressure distribution, through suitable horizontal and vertical adjustments.

Numerical investigations show the feasibility of solving real-world problems involving optimization of transonic airfoil shapes and accurate computational fluid dynamics simulation models of such surfaces. The results show that the proposed approach outperforms traditional surrogate-based approaches.

The proposed aerodynamic design optimization algorithm is novel and holistic. In particular, the ARP correction technique is original. The algorithm is useful for fast design of aerodynamic surfaces using high-fidelity simulation data in moderately sized search spaces, which is challenging using conventional methods because of excessive computational costs.

The positive outcomes demonstrated in programme efficacy trials and the apparent ineffectiveness of programmes in community settings have prompted investigators and practitioners to examine implementation fidelity. Critically important, but often overlooked, are the implementers who deliver evidence‐based programmes. This article distinguishes fidelity at the programme level from implementer fidelity. Two components of implementer fidelity are defined. It is proposed that implementer adherence and competence are related but unique constructs that can be reliably measured for training, monitoring and outcomes research. Observational measures from a school‐based preventive intervention are used to illustrate the contributions of implementer adherence and competence. Distinguishing implementer adherence to the manual from competence in programme delivery is the next step in child mental health programme implementation research.

Compared with the low-fidelity model, the high-fidelity model has both the advantage of high accuracy, and the disadvantage of low efficiency and high cost. A series of multi-fidelity surrogate modelling method were developed to give full play to the respective advantages of both low-fidelity and high-fidelity models. However, most multi-fidelity surrogate modelling methods are sensitive to the amount of high-fidelity data. The purpose of this paper is to propose a multi fidelity surrogate modelling method whose accuracy is less dependent on the amount of high-fidelity data.

A multi-fidelity surrogate modelling method based on neural networks was proposed in this paper, which utilizes transfer learning ideas to explore the correlation between different fidelity datasets. A low-fidelity neural network was built by using a sufficient amount of low-fidelity data, which was then finetuned by a very small amount of HF data to obtain a multi-fidelity neural network based on this correlation.

Numerical examples were used in this paper, which proved the validity of the proposed method, and the influence of neural network hyper-parameters on the prediction accuracy of the multi-fidelity model was discussed.

Through the comparison with existing methods, case study shows that when the number of high-fidelity sample points is very small, the R-square of the proposed model exceeds the existing model by more than 0.3, which shows that the proposed method can be applied to reducing the cost of complex engineering design problems.

The current study aims to examine the influence of contextual and provider‐level factors on the implementation fidelity of a research‐based substance abuse prevention program. Also, it aims to investigate whether two provider‐level factors, self‐efficacy and beliefs about the value of the program, statistically moderate and mediate the effects of a provider training intervention on implementation fidelity.

Using generalized mixed‐linear modeling, the authors examine relationships between program provider‐, organizational, and community‐level factors and implementation fidelity in a sample of 50 high school teachers from 43 high schools in eight states across the USA. Fidelity of implementation was assessed utilizing an observation procedure.

Implementation fidelity was negatively associated with the urbanicity of the community and the level of teachers’ beliefs about the value of the program, and positively predicted by the organizational capacity of the school. Comprehensive training significantly increased teachers’ self‐efficacy, which resulted in an increase in implementation fidelity.

School‐based prevention program implementation is influenced by a variety of contextual factors occurring at multiple ecological levels. Future effectiveness and dissemination studies need to account for the complex nature of schools in analyses of implementation fidelity and outcomes.

The authors’ findings suggest that both provider‐ and organizational‐level are influential in promoting implementation fidelity. Before implementation begins, as well as throughout the implementation process, training and ongoing technical assistance should be conducted to increase teachers’ skills, self‐efficacy, and comfort with prevention curricula.

The present study is one of the few to examine contextual and provider‐level correlates of implementation fidelity and use mediation analyses to explore whether provider‐level factors mediate the effects of a provider training intervention on implementation fidelity.

Numerical models are being increasingly relied upon to evaluate wind turbine performance by simulating phenomena that are infeasible to measure experimentally. These numerical models, however, require a large number of input parameters that often need to be calibrated against available experiments. Owing to the unavoidable scarcity of experiments and inherent uncertainties in measurements, this calibration process may yield non-unique solutions, i.e. multiple sets of parameters may reproduce the available experiments with similar fidelity. The purpose of this paper is to study the trade-off between fidelity to measurements and the robustness of this fidelity to uncertainty in calibrated input parameters.

Here, fidelity is defined as the ability of the model to reproduce measurements and robustness is defined as the allowable variation in the input parameters with which the model maintains a predefined level of threshold fidelity. These two vital attributes of model predictiveness are evaluated in the development of a simplified finite element beam model of the CX-100 wind turbine blade.

Findings of this study show that calibrating the input parameters of a numerical model with the sole objective of improving fidelity to available measurements degrades the robustness of model predictions at both tested and untested settings. A more optimal model may be obtained by calibration methods considering both fidelity and robustness. Multi-criteria Decision Making further confirms the conclusion that the optimal model performance is achieved by maintaining a balance between fidelity and robustness during calibration.

Current methods for model calibration focus solely on fidelity while the authors focus on the trade-off between fidelity and robustness.