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The issue of reliability of computer predictions of physical events is examined as the goal of verification and validation processes. It is argued that verification, both solution and code verification, can be carried out with a high degree of confidence, even though much remains to be done to improve and advance verification procedures. It is validation of mathematical models that stands as the major bottleneck of reliable computer predictions. Uncertainty of input data represents a major feature of validation processes and must be quantified if models are to be judged valid or invalid. Examples are given from solid mechanics and heat transfer that demonstrate validation processes employing stochastic models and fuzzy set theories.
}, issn = {2617-8710}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/ijnam/899.html} }The issue of reliability of computer predictions of physical events is examined as the goal of verification and validation processes. It is argued that verification, both solution and code verification, can be carried out with a high degree of confidence, even though much remains to be done to improve and advance verification procedures. It is validation of mathematical models that stands as the major bottleneck of reliable computer predictions. Uncertainty of input data represents a major feature of validation processes and must be quantified if models are to be judged valid or invalid. Examples are given from solid mechanics and heat transfer that demonstrate validation processes employing stochastic models and fuzzy set theories.