# Verification Validation and Uncertainty Quantification **Entity class:** Concept or analytic term **Domain:** Austin Research / Concepts **Doc Type:** Corpus analysis **Source basis:** Supplied research cluster with linked references; see the [[wiki/Austin Research Evidence Map|evidence map]] for verification scope. **Verification, Validation, and Uncertainty Quantification (VVUQ)** comprises three related but distinct tests of computational prediction. Verification concerns whether the mathematical problem is implemented and solved correctly. Validation concerns the model’s adequacy for its intended physical use. Uncertainty quantification tracks how uncertain observations, parameters, approximations, and model limitations affect outputs. [[wiki/Predictive Science Academic Alliance Program|PSAAP]] made these disciplines explicit in [[wiki/Predictive Science|predictive science]], and [[wiki/PECOS|PECOS]] implements them in experiment-coupled simulation. Their purpose is to connect a prediction to defensible confidence under specified conditions. They do not provide an unlimited guarantee outside those conditions. In a [[wiki/Digital Twin|digital twin]], new measurements can expose disagreement requiring recalibration or model revision. In [[wiki/Surety BioEvent App|BioEvent]], the adjacent problem is reliability of the data source; that source-trust filter is complementary to model assurance and should not be treated as mathematically identical to VVUQ. ## Connected entries [[wiki/Predictive Science Academic Alliance Program|Predictive Science Academic Alliance Program]] · [[wiki/PECOS|PECOS]] · [[wiki/Predictive Science|Predictive Science]] · [[wiki/Scientific Machine Learning|Scientific Machine Learning]] · [[wiki/Reduced-Order Modeling|Reduced-Order Modeling]] · [[wiki/Digital Twin|Digital Twin]] · [[wiki/Source Trust Tuple|Source Trust Tuple]] · [[wiki/Advanced Simulation and Computing|Advanced Simulation and Computing]] · [[wiki/ExCIS|ExCIS]] · [[wiki/John M. Huckabay Lake Travis Test Station|John M. Huckabay Lake Travis Test Station]] · [[wiki/Lawrence Livermore National Laboratory|Lawrence Livermore National Laboratory]] · [[wiki/Multi-Use Inference and Control|Multi-Use Inference and Control]] · [[wiki/Nuclear Stockpile Stewardship|Nuclear Stockpile Stewardship]] · [[wiki/Oden Institute|Oden Institute]] · [[wiki/PHO-ICES|PHO-ICES]] · [[wiki/Robert Moser|Robert Moser]] · [[wiki/Sandia National Laboratories|Sandia National Laboratories]] · [[wiki/Xpress and Xtend|Xpress and Xtend]] ## Sources and corpus context - [[research/The Austin Executable Loop|The Austin Executable Loop — master document]] - [NNSA Announces Selection of the Next Round of Predictive Science Academic Alliance Program Centers](https://www.energy.gov/nnsa/articles/nnsa-announces-selection-next-round-predictive-science-academic-alliance-program) - [Predictive Engineering and Computational Sciences](https://oden.utexas.edu/research/centers-and-groups/predictive-engineering-and-computational-sciences/) - [Introducing VECMAtk — Verification, Validation and Uncertainty Quantification for Multiscale and HPC Simulations](https://link.springer.com/chapter/10.1007/978-3-030-22747-0_36) **Cluster:** [[wiki/Austin Executable Loop|Austin Executable Loop]] · [[wiki/Austin Research Evidence Map|Austin Research Evidence Map]] ## Relationships <!-- BEGIN HUMANIZED RELATIONSHIPS 2026-09-11 --> This entry's documented connections are expressed in its definition and related-work routes, with provenance retained in the source-linked material. <!-- END HUMANIZED RELATIONSHIPS 2026-09-11 --> ## Simple Reminders, Quotations, and Thoughts > "As long as something can be relayed that resolves uncertainty, that is the fundamental nature of information. While this sounds surprisingly obvious, it was an important point, given how many different languages people speak and how one utterance could be meaningful to one person, and unintelligible to another. Until Shannon's theory was formulated, it was not known how to compensate for these types of "psychological factors" appropriately. Shannon built on the work of fellow researchers Ralph Hartley and Harry Nyquist to reveal that coding and symbols were the key to resolving whether two sides of a communication had a common understanding of the uncertainty being resolved." > **— Andrew Lih**, *2012, Edge Annual Question, “What Is Your Favorite Deep, Elegant, or Beautiful Explanation?”* [[reminders/Information/Information Resolves Uncertainty Across Every Medium by Andrew Lih|Information Resolves Uncertainty Across Every Medium by Andrew Lih]]