
Contents
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5.1 Introduction 5.1 Introduction
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5.2 Theory and methods 5.2 Theory and methods
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5.2.1 Stochastic collocation 5.2.1 Stochastic collocation
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5.2.2 Polynomial chaos 5.2.2 Polynomial chaos
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5.2.3 Sobol indices 5.2.3 Sobol indices
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5.2.4 Gaussian processes 5.2.4 Gaussian processes
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5.3 Verification and validation procedures 5.3 Verification and validation procedures
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5.4 Uncertainty quantification in molecular dynamics 5.4 Uncertainty quantification in molecular dynamics
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5.5 Software, tools and services for VVUQ 5.5 Software, tools and services for VVUQ
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5.6 Quantifying the influence of interaction potential parameters 5.6 Quantifying the influence of interaction potential parameters
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5.6.1 Derivative free deep active subspace and kernel-based methods 5.6.1 Derivative free deep active subspace and kernel-based methods
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5.6.2 Investigating high-dimensional parameter spaces 5.6.2 Investigating high-dimensional parameter spaces
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5.6.3 Epistemic vs aleatoric uncertainty 5.6.3 Epistemic vs aleatoric uncertainty
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5.6.4 The active subspace and global sensitivity analysis 5.6.4 The active subspace and global sensitivity analysis
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5.7 Summary 5.7 Summary
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5 Verification, Validation and Uncertainty Quantification
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Published:April 2025
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Abstract
This chapter presents a comprehensive overview of Verification, Validation and Uncertainty Quantification (VVUQ) in the context of molecular dynamics. It begins with an introduction to the fundamental concepts of VVUQ, followed by detailed explanations of various theoretical methods including stochastic collocation, polynomial chaos, Sobol indices and Gaussian processes. The chapter outlines standard procedures for verification and validation, emphasizing their critical role in ensuring the accuracy and reliability of simulations. It delves into techniques for uncertainty quantification specific to molecular dynamics, discussing software tools and services that facilitate these processes. Furthermore, the chapter explores the impact of interaction potential parameters on simulation outcomes, addressing both epistemic and aleatoric uncertainties. Advanced methods such as derivative-free deep active subspace, kernel-based approaches and global sensitivity analysis are examined for their effectiveness in high-dimensional parameter spaces. The chapter concludes with a summary of key insights and future directions for VVUQ in molecular dynamics.
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