Mathematical models have become indispensable sources of information on which technical and business decisions are based. It is therefore vitally important for decision-makers to know whether relying on the predictions of mathematical models is justified. When properly used, numerical simulation can be a major corporate asset. However, it can become a major corporate liability if the reliability of predictions is not guaranteed. Learn more in our latest blog post.
Smart Engineering Simulation Applications, (SESA or Sim Apps), are software tools crafted by expert analysts to standardize recurring simulation tasks. Learn more about how Sim Apps and AI tools can work together to enhance engineering simulation workflows.
The idea of achieving convergence by increasing the polynomial degree (p) of the approximating functions on a fixed mesh, known as the p-version of the finite element method, was at odds with the prevailing view in the finite element research community in the 1960s and 70s. But why?
It is generally recognized that explainable artificial intelligence (XAI) will play an important role in numerical simulation where it will impose the requirements of reliability, traceability, and auditability. These requirements will necessitate clear thinking about the nature of mathematical models, the trustworthiness of their predictions, and ways to improve their reliability.
The development of the finite element method (FEM) consists of two main branches: the art of finite element modeling and the science of finite element analysis. Learn why in this blog.
“At DST Group, we have effectively used StressCheck over the last 10 years to determine accurate stress intensity factors. The results have been used to improve our residual strength and structural life estimates for aircraft in service with the Royal Australian Airforce, including C-130, P-3C and F/A-18 A/B. We have found it to be extremely easy to use and a very versatile code with which to create parametric models.
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