Serving the Numerical Simulation community since 1989 
Welcome to the official blog of ESRD, where we discuss trending topics in numerical simulation and in the CAE industry.
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.
Some years ago, I attended a meeting of senior engineers at a Fortune 100 company. The topic of discussion was: What should be done about the significant discrepancies between outcomes predicted by finite element models and those observed in physical tests? The chief engineer, who called the meeting, vented his frustration, declaring he was tired of hearing that the finite element mesh was the problem. He no longer believed the predictions from finite element modeling and had lost confidence in the engineers who produced those results. How could this have been prevented?
In one of my conference presentations, I discussed variational crimes, noting that using point forces and point constraints in finite element analysis serves as examples of such crimes. In the question-and-answer session, I was asked: “If using point constraints is a variational crime, then how is it possible that the structure designed to refloat the Costa Concordia was full of those crimes and yet it worked just fine.” This question presented an opportunity for me to explain that finite element modeling (FEM) and finite element analysis (FEA) are complementary methods when analysts correctly understand their respective domains of application and use them accordingly. However, problems arise when FEM is used outside its scope, which is an all too frequent error.
We at ESRD preach and practice solution verification. We believe that reporting data computed by an approximate method is incomplete without providing an estimate of the size of the relative error. This simple and self-evident statement tends to trigger fierce resistance from those who were schooled in the use of legacy finite element modeling tools. But why?
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.
Contrary to various claims, IGA is not a new paradigm in the numerical approximation of partial differential equations; it is simply an alternative implementation of the p-version of the finite element method. IGA is one possible implementation of the p-version of the finite element method. Its distinguishing features are: (a) it retains the CAD geometry, (b) the basis functions are the same as those used for CAD representation, typically B-splines or NURBS, and (c) it provides for the enforcement of the inter-element continuity of the derivatives of the basis functions.
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