Serving the Numerical Simulation community since 1989 
Compliance with ASME and NASA standards on verification, validation, and uncertainty quantification (VVUQ) is often taken as sufficient evidence that simulation results can be trusted. This is a mistake, however. Compliance alone does not answer the question that truly matters to decision-makers: Are the predictions reliable enough for the decision at hand?
AI is rapidly transforming engineering workflows. However, a fundamentally important issue rarely gets discussed: AI explanations are only as trustworthy as the simulations they rely on. This blog post explores the rigorous technical requirements that must be met to ensure that numerical simulations provide the transparent, machine-interpretable evidence that explainable AI demands.
This blog post explains why legacy finite element codes are like a turtle’s carapace: a protective structure that provided a survival advantage in the resource-scarce 1960s but now impedes the flexibility needed to meet the demands of explainable AI (XAI). The good news is that the technology required to support XAI already exists.
Dr. Szabo shares some memories of his friend, mentor, collaborator, and co‑founder of ESRD, Ivo Babuška, who would have turned 100 this month.
I am often asked to comment on how it is possible that, although everybody agrees simulation governance is a good idea, it is not being practiced — or, as Shakespeare would put it more elegantly, “more honour’d in the breach than the observance.” — The short answer is that changing minds and habits is hard. A more detailed explanation follows.
Bridging the Gap is not just a book about numerical simulations, it’s a call to rethink how we understand them. While engineers run algorithms and mathematicians build them, a persistent divide between the two disciplines continues to limit the full potential of finite element analysis (FEA). Drawing on years of teaching and collaboration with industry professionals, the author exposes how vague terminology, entrenched assumptions, and stagnant modeling practices have created barriers to progress.
Anyone who relies on information generated through numerical simulation must know the difference between calibration and tuning, and understand the interactions between finite element modeling and finite element analysis. This blog post covers the main points.
Building an agentic system on top of legacy finite element modeling practices is like building a skyscraper on quicksand. Any team serious about reaching these capability levels must treat the soundness of numerical simulation as the highest priority, not an afterthought.
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.
In a letter published in Science in 1963, Bernard K. Forscher used the metaphor of building edifices to represent the construction of scientific models, also called laws. These models explain observed phenomena and make predictions beyond the observations made.
Building models consistent with the science of numerical simulation should never be confused with finite element modeling, an activity rooted in pre-1970s thinking. We should keep Forscher’s metaphor in mind when evaluating claims about the benefits AI integration is expected to bring to numerical simulation.
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