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.
Reliable strength prediction in laminated composites requires the simultaneous control of discretization and model-form errors. Conventional plate and shell formulations cannot generally provide this control. The hp-version of the finite element method offers a practical solution.
“From Legacy Finite Element Modeling to Explainable Simulation: Technical Requirements for XAI in Computational Mechanics”, a technical paper authored by ESRD Co-founder and Chairman Dr. Barna Szabó, was recently published in Computational Mechanics and is available to all as an open-access article. This paper discusses technical requirements posed by Explainable AI (XAI), methods for controlling both model-form and discretization errors in AI-assisted simulation, and model-centric vs. element-centric implementations.
Numerical simulation has substantial unrealized potential due to a disconnect between the professional practice of finite element modeling and the underlying science of numerical simulation. Realizing this potential requires bridging that gap—a challenge that entails overcoming several critical obstacles.
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.
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