The NAFEMS World Congress 2025 will host several interactive workshops on a wide range of engineering simulation and analysis topics. Led by experts and NAFEMS working groups, these interactive sessions will get attendees involved in the discussion like never before, making sure your voice is part of the push towards where simulation is going in the future.
| Validation of Engineering Simulations | |
| AI/ML in Simulation: Practical Observations and Thoughts on Simulation Governance | |
| The New AIAA Standard for Code Verification of CFD, The Importance of the Observed Order of Accuracy, and Exemplars to Show the Process | |
| Speaking of Simulation Live – Machine Learning | |
| ARENA2036: DigiTain/TEF AI Matters | |
| Standardisation for Manufacturing Process Simulation | |
| VMAP - A Vendor-Neutral Standard for Semantic Information Management in CAX Domain | |
| Unlocking your Organisations Simulation Capability through Maturity Assessment | |
| Improving How we Teach FEA | |
| Is Engineering an Art or a Procedure? | |
| Key Factors for Effective Engineering Virtualization | |
| How to Model What We Don’t Know: Probabilistic Foundations of Uncertainty Quantification and Machine Learning | |
| Simulation-Driven Product Validation Strategies | Details to come |
| Professional Simulation Engineer (PSE): How to Get Certified | |
| An Open Multiphysics Working Group Meeting – Everybody is Welcome | |
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This session is organized by members of the NAFEMS Simulation Governance and Management Working Group. There is currently a lot of excitement and enthusiasm around how Artificial Intelligence and Machine Learning will impact the way that simulation work is performed. On one end, such tools may become useful assistants, aiding the analyst in setting up, post-processing, and then documenting simulations using otherwise traditional tools. At the other extreme, AI/ML would replace traditional physics-based simulations, producing fast results based on its training set, with limited or no traceability on how it determined the answer. The simulation governance aspect of these technologies is currently uncertain and troubling. Existing tools for Verification and Validation are generally not fully applicable. New tools need to be developed to evaluate the adequacy of training sets versus the new problem for which results are being requested. Some providers make claims of ability to evaluate error, the robustness and accuracy of these evaluations needs to be studied and proven. Distinctions need to be made between tools that are based on data fitting only, versus ‘physics informed’ models. In this landscape of those promoting the product on one side, and those sceptical of change on the other, the SGMWG will seek to share practical observations on how simulation governance can currently be applied to these new tools, with the expectation that this will form a starting point for the eventual emergence of formal guidance as tools and methods mature. For example, many simulation users are accustomed to creating response surfaces or reduced order models which then become a fast and efficient means to seek out optimum designs. Once a proposed design has been identified, a full fidelity model is run as part of validating the design. In many ways AI/ML models can be compared to a high dimensional response surface. Thus such tools can be an efficient way to rapidly iterate a design, the final version of which would then be subjected to a full fidelity analysis using traditional simulation tools and appropriate VVUQ efforts.
This session is organized and executed by members of the NAFEMS Simulation Governance and Management Working Group. The main focus will be a presentation on the Guidelines for Validation of Engineering Simulation which has just been approved for publication. This publication defines an expanded means of validation, moving beyond dedicated high quality experiments commonly referenced in existing standards. A spectrum of validation methods is presented, categorizing them from the strict definition of Validation per ASME VVUQ standards through weaker validation approaches such as expert review. Attributes of validation rigor are defined, to aid in assessing the credibility of simulations. The goal is to provide greater flexibility to industrial users of simulations, allowing them to select a level of validation rigor consistent with the applications and risks associated with their simulations, as not all users need the highest levels of rigor associated with the aerospace or medical device industries.
AIAA has recently published a recommended practice for code verification for CFD, R-141-2021, and is now working on the publication of a full new international standard. This standard will be the first international standard for code verification (to the authors knowledge) in any field of engineering simulation and, as such, it represents an important step forward in terms of how to achieve confidence in the simulation tools that the NAFEMS membership uses.
Of key importance is the concept of the observed order of accuracy. Code verification requires the error in a simulation to be quantitatively evaluated, which by implication requires the solution to the simulation to be known. The error is calculated on a series of progressively refined meshes so that the trend in the error with the spatial discretization can be determined. Specifically, this trend is plotted on a log log graph and the observed trend should conform to a straight line, the gradient of which is the observed order of accuracy. This should then be compared with the formal order of accuracy claimed by the developer of the code.
NAFEMS SGM and CFD working groups have been working on some code verification exemplars to demonstrate the processes set out by AIAA: specifically, they are real cases using real commercial software tools where the observed order of accuracy is found not to correspond to the formal order claimed by the software developer. In each case, the reason for the discrepancy is explored and identified, so that suitable modifications to how the software tool is used can be made, and the tool can be used with confidence. The exemplars demonstrate the essence of the new standard and why considering the observed order of accuracy is important. Whilst the focus of the standard is on the performance of the simulation solver, as the exemplars show, considering the observed order of accuracy can also identify potential issues with the pre and post processing steps.
During our “Speaking of Simulation Live – Machine Learning” session at the NAFEMS World Congress, we will discuss how Machine Learning (ML) can help engineers to tackle everyday challenges. Experts will share practical examples of ML speeding up simulations and saving time. We will discuss how blending physics-based and data-driven models can offer real benefits, but also examine where such approaches can run into problems. Our speakers will talk about what it takes to prepare teams for ML, handle data concerns, and ensure that new tools are trustworthy. We will aim to highlight cases where ML has worked well, while also looking at issues like black-box models and the risk of overconfidence. Attendees can expect a clear view of how ML can support simulation engineers with tasks such as setting up models or analysing results. We will be realistic about the effort involved in adopting these methods, focusing on why validation and careful planning are so important. This session aims to share real-world experience on how ML can help with practical engineering needs, without losing sight of its limits. We look forward to an open conversation about how to make the best use of ML in engineering simulation.
Panelists
Introduction
Peter Froeschle (CEO ARENA2036)
DigiTain - Digitalization for Sustainability (40 min.)
Processes, Methods and Models for the Fully Digital Product Development of Sustainable Electric Drive Architectures
• Digital Methods & Models - Overview of Applied and Developed Methods (20 min)
Dr. - Ing Karsten Keller (University of Stuttgart - ISD)
• Digital Certification - Status of Homologation / Certification by Analysis for Crashworthiness (20 min)
Prof. Dr. -Ing André Haufe (Dynamore / Ansys)
AI Act & TEF AI Matters Services (40 min.)
Muhammad Saeed (ARENA2036)
Key takeaways include:
• Understanding the AI Act's impact on engineering and simulation workflows
• Exploring TEF AI Matters services for AI validation and deployment
• Engaging in discussions on AI compliance, governance, and risk mitigation
This workshop is ideal for engineers, researchers, and decision-makers looking to align their AI-driven innovations with European regulations while benefiting from TEF AI's expertise. s.
Engineering Simulation is a crucial capability for developing, refining and optimizing products. It is therefore, vital that organisations engineering simulation capability is fast and efficient, but can also be trusted to provide reliable results to enable important engineering decisions to be made with confidence. This is especially true with the increased use of AI techniques, as simulation is a critical source of trustworthy data used to train AI models. Is your engineering simulation capability ready?
Engineering Simulation is like a complex machine. It depends on many components working together in harmony to be effective, efficient, and reliable. To assess the status of their Engineering Simulation, organisations can benefit from conducting a Maturity Assessment. This enables them to identify their strengths, but importantly to identify areas to target improvement across.
This mini workshop will present an approach and framework that organisations can use to assess the status of their organisations engineering simulation capability, addressing the following topics:
• Organisation Goals
• Core Components of Engineering Simulation • Simulation Maturity Assessment Types
• Approach to conducting a Maturity Assessment
• An Example Maturity Framework
• Some Practicalities
• Taking Action
• The Benefits of Conducting a Maturity Assessment
Share your experiences and learn from others via interactive mini surveys and workshop discussions:
• Your goals and challenges
• Your experiences and lessons learnt
• Benefits of maturity assessment
Andy is a Fellow of the Institution of Mechanical Engineers. He has over 30 yrs experience in Automotive Engineering at Jaguar Land Rover, with 20 years at Senior Management level, including 10 years as Head of Engineering Simulation.
In 2019 formed PHRONESIM Ltd to provide consulting in all aspects of Engineering Simulation Strategy helping multinational organisations with; simulation capability maturity assessment, simulation strategy development, improvement roadmaps, and simulation strategy training and coaching.
Andy is a member of the NAFEMS UK Steering Committee, Assess Business Theme, and the Simulation Governance and Management Working Group .
In Manufacturing Process Simulation, we use multi-scale, multi-physics models to obtain high-fidelity predictions of the transformations in the material during the production of metal or composite parts. This often requires material data for which there is no standardised way of testing. Also, the material modelling and manufacturing process simulation approaches that exploit these data to predict what will happen during manufacturing, are rarely standardised.
The consequence of this for industry is low re-use of material data and modelling: in complex supply chains, material characterisations are often repeated and models often created again from scratch. Worse: opportunities to use simulation are lost because people are not confident re-using existing data and models and therefore simply refrain from using simulation.
The objective of this session is to reflect on the current state of standardisation in this particular field, and identify opportunities to improve things. The discussion will be started by four short presentations.
The need for standardisation for manufacturing process simulation in the aerospace industry
Sjoerd van der Veen (Airbus)
Due to a lack of standardisation today, material characterisations are often repeated and models often created again from scratch. Worse: opportunities to use simulation are lost because people are not confident in re-using existing data and models and therefore simply refrain from using simulation.
Development of Modelling Guidelines for Welding in the Nuclear Industry
Paull Hurell (Amentum), Michael Roy (TWI)
Weld modelling guidelines and measurement validation benchmarks are a valuable part of the analyst’s toolkit, to aid good decision making. This presentation describes weld simulation examples for austenitic and ferritic steels, including comparisons with experimental measurements, developed for the civil nuclear industry.
Generation, validation and management of material data and models in composites process simulation
Goran Fernlund (Convergent Manufacturing Technologies), Martin Roy, Alastair McKee (Convergent Manufacturing Technologies)
Process simulation often falls short of its full potential due to the absence of established and efficient methods. To unlock its capabilities, we must transition from 'one-off artistry' to standardized, validated workflows. This presentation explores the current state of composites process simulation, focusing on the generation, validation, and management of material data and models. It also highlights future opportunities and pathways to achieve them.
NAFEMS Benchmark Examples of Buckling Occurrence, Residual Stresses and Bending Distortion in Additive Manufacturing
Yongle Sun (Cranfield), Oliver Found (TWI), Anas Yaghi (TWI)
An important part of standardisation procedures is to make available representative benchmark examples to the modelling and simulation community. In this presentation, we provide two benchmark examples that compare experimental measurements and numerical simulations of additive manufacturing processes.
Presentations:
Automation is a very strong trend in engineering and FEA calculations. We try to automate as many things as possible, simply to make them easier/faster/cheaper to do. But can engineering design be really automated, and to what extent this is possible? Also, what would be the consequences of this? Or to put it differently, is engineering an art, or a procedure? In this workshop we will dive deep into this issue, and have a discussion about this.
It seems there is a spectrum in FEA world. On one end, you can learn mathematics related to FEA on an expert level, and on the other end you can learn to use the software with most sophisticated options. But for me, it's a triangle, and at the top of it is the actual understanding on how things work, and what you're trying to achieve. This seems to be often omitted in how we teach FEA (and engineering in general). In this workshop I want to dive deeper into this idea, and have a discussion about this.
Virtualization of engineering has the potential to address strategic goals like reduction of time-to- market, managing an increasing product complexity and variety, or raising the flexibility to react on the market. Companies can realize it at various levels. But how shall a successful transformation look like? The workshop aims to provide an impulse for successful engineering virtualization and to identify potentials and fields of activity by analyzing and discussing industry examples with the participants. It will cover key aspects such as strategic goals, boundary conditions, process, organization, technology, data, or mindset. Depending on the discussion, experience-based recommendations will be added to the conclusion.
Aims
Who should participate?
1) Introduction to uncertainty, statistical modeling, and learning from data
a) Bayesian and Frequentist concepts of probability
b) Aleatory and epistemic uncertainty
c) Tools for statistical machine learning
d) Statistical modeling: data generating model and prior predictive simulation
e) Bayes’ rule as the foundation for statistical simulation
f) (Machine) Learning from data and posterior predictive simulation
2) Examples used
a) A real-life application of Bayes’ Rule
b) An engineering application with uncertain data
3) Tools used
a) R
b) Rstudio
c) Stan
What will you learn?
● You will learn about the basics of Uncertainty Quantification and Statistical Machine Learning starting from first principles such as concepts of probability and the application of Bayes’s Rule.
● You will see how statistical simulations can be run and evaluated using freely available software such as R, Stan, and RStudio.
● You will learn about application use cases of Statistical Machine Learning in industry.
What questions will this course answer?
● What is probability and how can I use it to assess and enhance the predictive power of simulations?
● How can I use statistical modeling to learn from data?
● How can statistical simulation bridge the gap between traditional simulation and “physics-agnostic” machine learning?
● How can I make predictions and decisions in the face of uncertainty?
Who should attend?
Everyone is welcome to attend. The seminar will be most interesting for
● Simulation engineers that would like to understand the predictive power of their models by quantifying uncertainty
● Engineers that would like to broaden their understanding of potential applications of statistical machine learning in their field
● Anyone interested in the practical application of physical modeling, machine learning, and data science
The workshop/short course is code independent. Examples will be presented with the use of the open-source statistics tools R, RStudio, and Stan.
This short workshop will provide information on the NAFEMS Professional Simulation Engineer (PSE) Certification scheme, presenting why it is useful, how it works, the application process and some client case studies. PSE enables simulation engineers to demonstrate competencies acquired throughout their professional career, located in the PSE Competency Tracker which will be described at https://www.nafems.org/professional-development/certification/
Members will present and discuss current activities of the MPWG. There will be members onsite, while a couple of us will be joining online. Starting with an introduction of the working group, we will present and discuss topics like next events (e.g., the Multiphysics Conference in 2026) and publications (e.g., The NAFEMS Journal of Multiphysics Case Studies – Volume 2). Additionally, we will discuss with the audience the future of Multiphysics simulation.
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