Smarter Simulations: How AI is Reinventing Geometry and Meshing

Author: Denis Avetisyan

Artificial intelligence is rapidly transforming the traditionally painstaking processes of preparing geometry and generating meshes for engineering simulations.

This review surveys recent advances in AI-assisted methods for automating geometry preparation, enhancing mesh quality, and improving the efficiency of simulation workflows.

Despite decades of advancement, creating accurate and efficient simulations remains bottlenecked by time-consuming geometry preparation and mesh generation. This survey, ‘A Survey of AI Methods for Geometry Preparation and Mesh Generation in Engineering Simulation’, comprehensively reviews the burgeoning field of artificial intelligence applications designed to address these challenges. Recent advances demonstrate AI’s potential to automate tasks ranging from part classification and defeaturing to improved mesh quality prediction and accelerated parallel processing. As AI increasingly augments traditional workflows, what novel data-driven approaches will define the next generation of engineering simulation?

The Bottleneck of Simulation: Addressing Manual CAD Preparation

A significant impediment to widespread simulation-driven design lies in the substantial time investment required for initial CAD model preparation. Current workflows often dedicate up to 80% of the total simulation timeframe to tasks such as geometry cleanup, simplification, and meshing – processes traditionally performed manually by skilled engineers. This bottleneck arises because raw CAD models, designed for manufacturing or visualization, typically contain excessive detail irrelevant to simulation, alongside imperfections that can compromise accuracy or even prevent analysis. The sheer volume of these pre-processing steps not only extends project timelines but also introduces opportunities for human error, demanding careful verification and potentially iterative rework before meaningful simulation can commence. Addressing this challenge through automation promises to unlock substantial efficiency gains and enable broader application of simulation across diverse engineering disciplines.

Modern engineering relies increasingly on detailed Computer-Aided Design (CAD) models, but their very complexity presents a significant hurdle for simulation workflows. Traditional preparation methods, often requiring substantial manual intervention, struggle to efficiently process the intricate geometries, fine details, and diverse material properties inherent in these models. This leads to inaccuracies as simplifications are made to manage computational load, and introduces delays while engineers painstakingly refine the digital representation for analysis. The proliferation of advanced features-like organic shapes, variable thickness walls, and complex assemblies-further exacerbates these challenges, demanding increasingly sophisticated and time-consuming pre-processing steps before reliable simulations can even begin.

The reliance on manual preparation of computer-aided design models introduces significant potential for errors, ranging from incorrect material properties to misrepresented geometric features. These inaccuracies, however small, can propagate through simulations, yielding unreliable results and necessitating costly rework. More critically, this hands-on approach severely restricts the ability to scale simulation-driven design initiatives; as the number of designs or the complexity of each model increases, the time and resources required for manual preparation become unsustainable. Consequently, innovation is stifled, and the full benefits of virtual prototyping-faster iteration, reduced costs, and improved product performance-remain largely unrealized. Addressing this bottleneck is therefore paramount for organizations seeking to fully embrace and capitalize on the power of simulation.

The promise of widespread, simulation-driven design hinges on overcoming the current bottleneck of extensive manual preparation of computer-aided design (CAD) models. Current workflows dedicate a disproportionate amount of time – up to 80% – to tasks like geometry cleanup, simplification, and meshing, hindering rapid iteration and exploration of design possibilities. Automating these initial steps isn’t simply about saving time; it directly addresses sources of error inherent in manual processes, leading to simulations that more accurately reflect real-world performance. By relieving engineers from these tedious tasks, automation facilitates a shift towards more designs being simulated, and with greater frequency, ultimately accelerating innovation and enabling a more robust and reliable engineering process.

Intelligent Part Classification: The Foundation of Automated Workflows

Accurate 3D part classification is a foundational element in automated simulation workflows because it directly informs the selection of appropriate material properties, mesh densities, and contact definitions. Incorrect classification can lead to the application of inappropriate simulation parameters, resulting in inaccurate results and potentially flawed engineering decisions. Specifically, different part types-such as fasteners, housings, or flexible components-require distinct idealization strategies for efficient and accurate analysis; for example, a beam element may be suitable for a stiffening rib but inadequate for a complex bracket. Automated classification enables the consistent and reliable assignment of these parameters, minimizing manual intervention and improving the overall quality and trustworthiness of simulation outcomes.

Supervised learning techniques offer a data-driven approach to part classification within complex assemblies by training algorithms on labeled datasets of geometric and topological features. Ensemble methods, such as Random Forests, demonstrate particular robustness due to their ability to mitigate overfitting and handle high-dimensional data; these methods construct multiple decision trees during training and aggregate their predictions to improve overall accuracy and generalization performance. This approach contrasts with rule-based systems by adapting to variations in part geometry and reducing the need for manual feature definition, allowing for automated classification of components even in the presence of noise or incomplete data, and ultimately improving the efficiency of downstream CAD preparation processes.

Traditional rule-based feature recognition relies on pre-defined geometric patterns and tolerances, proving inflexible when encountering design variations or complex geometries. B-rep Graph Methods, conversely, represent a boundary representation (B-rep) model as a graph where nodes are topological elements (faces, edges, vertices) and edges represent adjacency relationships. This graph-based approach allows for a more robust and adaptable identification of geometric features by analyzing topological relationships rather than strict geometric definitions. Consequently, B-rep Graph Methods demonstrate increased accuracy in feature recognition, particularly for parts with complex or non-standard designs, and offer greater adaptability to variations in model quality or design intent.

Automated part classification streamlines CAD model preparation by enabling the assignment of appropriate mesh densities, material properties, and connection definitions without manual intervention. This automated process reduces the time and potential for error associated with pre-processing, facilitating efficient finite element analysis (FEA) or computational fluid dynamics (CFD) simulations. Specifically, accurate classification allows for the selective refinement of meshes in critical areas of a component, optimizing computational cost while maintaining solution accuracy. Furthermore, consistent classification supports the application of standardized idealization strategies, such as simplified representation of small features or automated weld modeling, ultimately accelerating the overall simulation workflow.

Semantic Segmentation: Enabling Detailed and Accurate Simulations

3D part segmentation facilitates the application of localized parameters crucial for accurate simulation. By dividing a component into distinct regions, specific material properties – such as Young’s modulus, Poisson’s ratio, and density – can be assigned to each segment, reflecting real-world variations. Similarly, boundary conditions, like fixed supports or applied loads, can be selectively applied to individual parts. Furthermore, mesh density can be adjusted on a per-segment basis, employing finer meshes in areas of high stress concentration or geometric complexity and coarser meshes in less critical regions; this targeted approach optimizes computational efficiency while maintaining simulation fidelity.

The application of Transformer networks to 3D part segmentation represents a shift from traditional methods, leveraging architectures originally developed for sequence-to-sequence tasks in natural language processing. These networks are adapted to process Boundary Representation (B-rep) data, which defines the geometry of 3D objects through surfaces, edges, and vertices. By analyzing the relationships within the B-rep structure – specifically the connectivity and adjacency of these elements – Transformers can identify and delineate individual parts within a larger assembly. This approach moves beyond voxel-based or point cloud segmentation by directly interpreting the CAD-based geometric definition, allowing for more precise and robust segmentation results, even with complex geometries and imperfect data.

Fine-grained semantic segmentation enhances simulation accuracy by allowing for the precise definition of material behaviors and physical interactions within a model. By identifying and isolating individual component parts, simulations can move beyond simplified, homogenized material assignments and instead utilize component-specific properties such as elasticity, thermal conductivity, and friction coefficients. This detailed approach is crucial for realistically modeling complex phenomena including stress concentrations, heat transfer, fluid dynamics, and failure modes. The ability to accurately represent these interactions improves the correlation between simulation results and physical testing, leading to more reliable predictions and optimized designs.

Segmented 3D models generated through semantic segmentation directly facilitate subsequent computational steps in simulation workflows. The precise delineation of component parts allows for targeted mesh refinement; regions identified as critical for accurate results can be assigned higher mesh densities, while less sensitive areas utilize coarser meshes, optimizing computational cost. This part-specific meshing, driven by the segmentation, ensures appropriate discretization for accurate representation of geometry and material behavior. Furthermore, the segmented data provides the necessary boundaries for defining distinct material assignments, boundary conditions, and physical interactions within the simulation environment, leading to more reliable and insightful analysis.

Optimizing Mesh Quality: Predictive Algorithms and Automation

Predicting mesh quality prior to mesh generation leverages Boundary Representation (B-rep) features – geometric descriptions of the model’s surfaces and edges – to identify areas likely to produce poor-quality elements. Analysis of these features, including curvature, feature size, and geometric complexity, allows for pre-emptive refinement of the model or adjustment of meshing parameters. This targeted approach focuses computational resources on problematic areas, increasing mesh quality while reducing overall processing time. Specifically, features indicative of potential issues, such as small angles or high curvature, can be automatically identified, triggering localized mesh size control or geometry simplification before the meshing algorithm is applied.

Random Forest algorithms provide a robust method for predicting mesh quality based on boundary representation (B-rep) features prior to mesh generation. This machine learning approach utilizes an ensemble of decision trees, trained on datasets correlating geometric features – such as curvature, feature size, and proximity – with resulting mesh metrics like element skewness, Jacobian ratio, and aspect ratio. By analyzing these B-rep characteristics, the Random Forest model can accurately estimate areas likely to produce poor-quality elements. This predictive capability enables targeted mesh refinement – applying finer mesh densities or employing specific meshing strategies – to problematic regions before the computationally expensive meshing process begins, ultimately improving overall mesh quality and reducing the need for iterative manual adjustments.

Defeaturing simplifies geometric models by removing small features – such as fillets, chamfers, and holes – that do not significantly contribute to simulation accuracy but substantially increase mesh density and computational expense. This process is critical for generating high-quality meshes suitable for finite element analysis (FEA) and computational fluid dynamics (CFD). Automation of defeaturing is commonly achieved through dedicated pre-processing tools, including Cubit, Altair HyperMesh, and Siemens Simcenter NX, which utilize algorithms to identify and eliminate unnecessary geometric details based on user-defined criteria. Automated defeaturing reduces manual effort, minimizes the risk of human error, and enables efficient mesh generation for complex geometries and large assemblies.

Recent advancements in computational mesh generation have enabled hex-meshable volume ratios reaching up to 98.7%. This improvement is driven by sophisticated decomposition methods that effectively partition complex geometries into volumes suitable for hexahedral element filling. These methods analyze geometric features to optimize the partitioning strategy, minimizing the need for tetrahedral or pyramidal elements which often introduce numerical diffusion and reduce solution accuracy. Achieving such high hex-meshable ratios significantly improves the overall quality of the generated mesh, leading to more accurate and efficient simulations, and a reduction in computational resources required for analysis.

Automated scripting and parallel mesh generation significantly reduce the time required for mesh creation, particularly for complex assemblies. By leveraging scripting languages, repetitive tasks such as mesh parameter adjustments and quality checks can be automated, minimizing manual intervention. Parallel processing distributes the computational load across multiple cores or machines, enabling faster mesh generation for large models. Current implementations report reductions in tuning time of up to 90% compared to traditional manual methods, thereby accelerating the simulation workflow and improving overall efficiency. This scalability is crucial for handling assemblies containing millions of elements, allowing for rapid iteration and exploration of design variations.

Towards Fully Automated Simulation Workflows: The Future of Engineering Analysis

Simulation preparation, traditionally a bottleneck in the design process, is undergoing a transformation through the integration of automated workflows. By intelligently classifying component parts, segmenting complex geometries, and optimizing the resulting mesh, substantial reductions in both time and human effort are now achievable. This automated approach moves beyond manual intervention, streamlining the conversion of computer-aided design (CAD) models into simulation-ready formats. The process not only accelerates the initial setup but also minimizes the potential for human error, leading to more consistent and reliable simulation results. Ultimately, this automation enables engineers to focus on analysis and interpretation, rather than tedious preparatory tasks, fostering a more efficient and innovative design cycle.

The creation of high-fidelity simulations often hinges on the quality of the mesh – the discretization of a continuous physical domain into discrete elements. Recent advancements leverage volumetric parameterization and block-structured meshing to substantially improve both mesh quality and computational efficiency. Unlike traditional surface-based meshing, volumetric parameterization considers the entire three-dimensional space, allowing for the creation of smoother, more isotropic meshes, particularly in regions with complex geometry. This approach, coupled with block-structured meshing – dividing the domain into regular, interconnected blocks – simplifies the meshing process and facilitates efficient parallelization. The result is a reduction in element count without sacrificing accuracy, leading to faster simulation runtimes and reduced computational costs, especially critical for large-scale or transient analyses. This technique ensures that even intricate designs can be accurately represented with optimized meshes, ultimately streamlining the simulation workflow.

Recent advancements in computational methods demonstrate a remarkable capacity for predicting optimal mesh configurations with high accuracy. Specifically, a parallel mesh prediction technique has been developed that minimizes discrepancies between predicted and ideal meshes, achieving a Mean Absolute Percentage Error (MAPE) of just 2.13% when executed on shared-memory systems. Even when scaled to distributed-memory architectures, the predictive capability remains robust, with a MAPE of only 5.68%. These results indicate a significant step towards automated simulation workflows, as the system reliably generates high-quality meshes without extensive manual intervention, promising substantial reductions in computational setup time and increased simulation efficiency.

The streamlining of simulation preparation, achieved through automated workflows, promises to democratize access to advanced engineering analysis. Previously limited by the time and expertise required for tasks like mesh generation, a broader range of engineers and designers can now leverage simulation as an integral part of the design process. This expanded accessibility isn’t merely about convenience; it fosters a culture of rapid iteration and exploration, allowing for more design alternatives to be thoroughly vetted before physical prototyping. Consequently, innovation is accelerated across diverse industries, from aerospace and automotive to medical devices and consumer products, as optimized designs reach the market faster and with increased confidence in their performance and reliability. The reduction in time-to-market, coupled with the potential for significant cost savings, positions simulation-driven design as a key driver of competitive advantage in the modern industrial landscape.

The trajectory of modern simulation is decisively shifting towards fully integrated, intelligent workflows. These systems aim to eliminate the traditionally substantial bottleneck of manual preparation, automatically converting complex Computer-Aided Design (CAD) models into simulation-ready meshes with minimal human intervention. This isn’t simply about speed; it’s about achieving a higher degree of fidelity and reliability in the resulting data. By leveraging advances in automated part classification, segmentation, and mesh optimization-coupled with techniques like volumetric parameterization-these workflows promise to deliver accurate and robust simulations consistently. Such automation not only reduces time and cost but also democratizes access to simulation-driven design, allowing engineers and researchers across diverse fields to explore and innovate with greater efficiency and confidence. Ultimately, the vision is a seamless pipeline where the complexities of model preparation fade into the background, leaving researchers free to focus on interpreting results and driving breakthroughs.

The pursuit of automated geometry preparation, as detailed in the survey, echoes a fundamental tenet of system design: reducing complexity to achieve robustness. Ken Thompson observed, “If it’s not elegant, it’s not working.” This sentiment perfectly encapsulates the challenge presented by AI-assisted meshing. The article highlights how deep learning and reinforcement learning approaches attempt to distill the intricate process of mesh generation into manageable, automated steps. Yet, as the study implicitly acknowledges, simply automating existing, complex CAD scripting doesn’t guarantee a truly elegant solution; it merely shifts the complexity. A truly effective system, like a well-designed mesh, must prioritize simplicity and clarity to avoid fragility, a principle that guides the evolution of this field.

What Lies Ahead?

The surveyed approaches, while promising, largely treat geometry preparation and meshing as isolated problems, amenable to localized optimization. This is a fundamental limitation. A truly robust system will not simply predict a good mesh; it will understand the underlying physics the simulation intends to model, and construct a representation inherently suited to that purpose. Current reliance on existing CAD and meshing pipelines-essentially automating established, often imperfect, processes-feels like polishing the chains rather than building a new vehicle. The field chases metrics of mesh quality, but rarely asks why a particular quality is crucial for a given simulation.

Future progress necessitates a shift towards holistic, physics-informed AI. Volumetric parameterization offers a pathway, but requires deeper integration with simulation solvers. Reinforcement learning, currently focused on narrow tasks, must expand its scope to encompass the entire simulation workflow. The real challenge isn’t automating what is already done, but discovering entirely new methods of representing and solving engineering problems. It requires acknowledging that the geometry is not the end of the process, but a means to an end-a bridge between abstract mathematics and physical reality.

The current enthusiasm for deep learning risks becoming another layer of complexity obscuring fundamental principles. Good architecture is invisible until it breaks, and only then is the true cost of decisions visible.

Original article: https://arxiv.org/pdf/2512.23719.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/