What are some of the best practices for validating and verifying your simulation results?

Validation is the process of comparing two results. In this process, we need to compare the representation of a conceptual model to the real system. If the comparison is true, then it is valid, else invalid. Verification is the process of comparing two or more results to ensure its accuracy. In this process, we have to compare the model’s implementation and its associated data with the developer's conceptual description and specifications.

You can carry out verification at an early stage of a project. Since the verification process includes a review of documents, design, and code, you can start the process even before any implementation and continue during the rest of the development phase. Validation, on the other hand, requires a functional product, and as a result, validation is done at a later stage of the project. You need to have a product or code that you can execute in order to validate it

verification is carried to prove that what all fictional critical operations are as per designed spec. Validation on the other had is a documented evidence of all the Fictional Testing to prove that the quality of the equipment or the process meet its desired end product.

1. Verification of Database Requirements Imagine we need to build an application:- We need a signup form that users can enter details in to subscribe to the client's newsletter. The application should be able to save user input to an SQL database. The client requires that the application collects a user's email, full name, and city. We can perform verification testing on the document. 2. Validation of Submit Button Considering the same application from Example 1, we can perform a validation test on the submit button. We can perform this type of validation in several ways. One approach is to write automated tests that assert for whether the data is saved after clicking the button. Another approach is to test by submitting invalid data.

1) Lack of Precise Oracles: As scientists use computational calculations to explore new ideas and theoretical methods, the outcome of a calculation is generally not known at all, or at least not precisely known. 2) Large Configuration Space: As discussed above, the experimental nature of scientific software (codes) typically results in a large configuration space. 3) Large-Scale, Heterogeneous Data: Scientific software often processes large-scale, heterogeneous data,

Typical Validation Lead: Besides the obvious/typical soft and hard skills, including understanding of the regulatory requirements: Good documentation skills - The documentation needs to be able to stand on its own even after a couple of years. Methodology oriented and ease of adopting different/new ways to achieve the same end goal - Even if the overall validation steps are the same, different companies and validations may require different methodology High integrity - ability to create trust and confidence, is key to a successful risk based validation

Good Validation skills Lead: Besides the above, Good PM skills - getting a close alignment of the validation activities with the overall project objectives, planning and budget is key for a successful and timely execution Process and activity focused. - Validation is not a set of documents that must be produced. It’s the steps and activities that produce the value. Ability to explain, train and coach the project team, of the purpose and value of the steps and activities in a tangible way - Validation is not only a Validation (Lead/team) exercise. Project and system quality comes from all involved stakeholders, and cannot be documented/tested in. Quality needs to be build/trained into the SW, processes and people using it.