Computer-aided engineering (CAE) uses physics-based software to analyze, validate, and optimize how a part or system will perform under real-world conditions - structural loads, thermal effects, airflow, vibration - before it's physically built.

DEP delivers this across aerospace and defence through dedicated simulation services covering structural simulations, sheet metal forming simulation, CFD/thermal/flow simulation, aerodynamic design and analysis, and acoustics and vibration analysis, alongside design and engineering services spanning CAD support, weight reduction, and manufacturing engineering. The distinguishing factor for aerospace and defence programs specifically is that these disciplines rarely run in isolation: a structural weight change shifts the CG and the flutter margin, a thermal load changes the material allowables used in the stress case, and a fatigue life target constrains what the aerodynamicist can do with a leading-edge radius - which is precisely why the platform-level integration discussed in Q6 and Q12 matters as much as any single solver.

Pre-processing is the stage where a CAD geometry is prepared for simulation - cleaning up the geometry, dividing it into a mesh of finite elements, and assigning material properties and boundary conditions - before a solver can run.

For complex aerospace assemblies (airframe structures, engine casings, cabin structures), pre-processing quality directly determines simulation accuracy, which is why DEP's MeshWorks platform treats meshing and model-building as core, dedicated modules rather than an afterthought to the solver. In practice this means surface and volume meshing, automated mid-surface extraction for thin-walled aerostructure panels, and automated cleanup and mesh-quality checking are handled as a connected sequence rather than separate manual steps - and the modelling layer above it handles smart assembly, connector creation, and material/load assignment across full airframe- or engine-scale models, the same class of assembly complexity used for full-vehicle and ship-level models in other verticals.

Morphing reshapes an existing finite element or CFD model to match a new target geometry - such as a design change or material substitution - without rebuilding the mesh from scratch. Because the mesh topology and connectivity are preserved through the shape change, morphing keeps element quality, boundary conditions, and load definitions intact across the redesign - the mesh doesn't need to be re-validated from zero each time, which is what compresses iteration time.

DEP applied this directly in a documented success story converting aluminum bulkheads to composite bulkheads, achieving a 28% mass savings while improving performance - a concrete example of morphing-driven design iteration on a real aerospace structural component, delivered through MeshWorks' feature-based morphing capability. Across MeshWorks more broadly, morphing supports component-level, full-model, and CAD-level part morphing, is CAD-agnostic so it works across mixed CAD environments without conversion errors, and is documented to cut iteration time by up to 80% versus rebuilding a model from scratch - the mechanism behind why 250-plus design variants can be generated from a single baseline model without returning to CAD, as described in Q7.

CAE parametrization converts a fixed finite element or CFD model into a flexible, adjustable model where geometry, section, connector, and topology parameters can be changed and re-evaluated automatically, instead of rebuilding the model for every design variation.

MeshWorks supports this across crash, NVH, and durability domains, letting aerospace engineering teams run structural and weight trade-off studies - including DEP's documented weight optimization work on an aircraft stabilizer driven by parametric modeling - without starting each configuration from scratch. The conversion from a standard, one-off CAE model into a parametric one is a one-click operation in MeshWorks, and the resulting parameter set links directly into Design of Experiments (DOE) and MDO workflows (Q6), which is what turns a single stabilizer model into a structured, sweepable design space rather than a series of disconnected point studies.

Reduced Order Modelling condenses a detailed finite element model into a lightweight, high-accuracy parametric representation - for example, converting a complex 3D structural assembly into an equivalent 1D beam model without losing predictive reliability.

In MeshWorks, ROM reduces solver time by up to 50%, which matters directly for aerospace programs running repeated crash, durability, or structural load cases across large design spaces. ROM's role extends beyond a single-model speed-up: when parametrization and morphing (Q3–Q4) generate a large population of design variants, ROM is the engine that runs that entire population fast enough - compressing what would be days of solver time into hours - to make the population usable as AI training data, which is the direct handoff into the AIWorks pipeline covered in Part 2.

MDO evaluates and optimizes a design across multiple engineering domains simultaneously - structural, thermal, and acoustic performance, for example - instead of optimizing each domain in isolation and reconciling conflicts afterward.

MeshWorks' MDO module shares parameters across crash, NVH, CFD, and durability domains and integrates Reduced Order Models with Design of Experiments (DOE) data, incorporating lattice and topology optimization alongside multi-objective target-setting, so aerospace programs can hit weight and cost targets while keeping structural, thermal, and acoustic performance within requirements. Running these domains simultaneously - rather than sequentially - is what surfaces a trade-off before it becomes a late-stage redesign: as Part 2 covers, this is also the same multi-discipline structure that DEP's AIWorks Optimize module drives with AI-vs-solver switching layered on top.

AI in CAE applies machine learning to specific steps of the simulation workflow - generating training data, training predictive models, and running AI-powered optimization - to reduce the manual effort and solver time those steps traditionally require, without replacing the underlying physics-based methods described in Part 1.

AIWorks is built as a complete AI lifecycle rather than a point tool: it covers data generation for AI training, model building and validation, deployment as fast solvers, and design optimization using those trained models, all within one platform. DEP positions AIWorks as deployed in active engineering programs - including aerospace programs and Tier-1 suppliers - rather than as an evaluation pilot or research prototype, delivering 10x simulation speed at under 3% AI prediction error on aerodynamic drag and body-stiffness predictions, validated across programs including Automotive OEMs, Aerospace, Railways, Energy, Electronics, and Heavy Equipment. Because the AIWorks benchmark figures (speed multiplier, error rates) are validated across DEP's full vertical list rather than aerospace-exclusively, aerospace and defence programs should read them as representative of the technology's demonstrated ceiling, with program-specific accuracy depending on load case and geometry class.

Physics-Informed Neural Networks embed actual engineering physics - finite element equations, Navier-Stokes, and LES (Large Eddy Simulation) - directly into the neural network's training loss, rather than relying on statistical curve-fitting alone. This matters because standard ML can extrapolate blindly once a new design falls outside its training distribution - a silent failure mode in an engineering context — whereas forcing the network to respect governing physics equations during training is what allows it to extrapolate credibly onto genuinely new geometries.

DEP's AIWorks implementation goes a step further by using separate PINN architectures tuned for mildly, moderately, and highly nonlinear problem classes - a crash or impact case is treated with a fundamentally different physics formulation than a linear frequency analysis, because the underlying mathematics differ. Each trained model is paired with an Equivalence Scorer - a confidence-rating mechanism generated automatically alongside the predictive and generative models per load case - that flags when a design has drifted outside the AI model's reliable prediction envelope and automatically routes that case back to the physics solver instead of accepting an untrustworthy AI result. DEP's validated accuracy benchmarks, checked against real production solver runs rather than curated benchmark datasets, show under 3% error on aero/drag coefficient and body-stiffness predictions, a 95%+ R² score on crash/impact prediction for highly nonlinear side-impact load paths, and under 5% error on NVH/frequency prediction across multiple structural configurations.

Building on the meshing and parametrization fundamentals in Part 1, AIWorks' Features-AI capability automatically recognizes and parametrizes engineering geometry features directly on the finite element model - including ribs, bosses, fillets, spot welds, MIG welds, holes, and flanges - across assemblies, and supports multiple manufacturing processes including casting, stamping, injection moulding, and welded-assembly geometries. Once recognized, these features are converted into smart parametric handles, which is what enables rapid new design creation and AI-assisted concept generation without an engineer needing to return to CAD for every iteration - and it's this same feature-recognition step that drives the mass training-data creation feeding the PINN models described in Q8.

AIWorks' GEOM-AI capability creates digital twins directly from manufactured scan data, using AI-driven mesh morphing and a patented voxelisation-based process to map scanned geometry, thickness, and result fields into a unified statistical space. This extends the morphing fundamentals from Part 1 by automatically aligning scanned part geometry with the nominal CAD or mesh model and correcting for manufacturing deviations - mapping geometry, thickness variation, pre-strain, and distortion from the as-built part onto the nominal FE model - turning physical aerospace/defence hardware into a simulation-ready model without manual re-modeling. GEOM-AI also supports the reverse direction: modifying a CAD shape through morphing and receiving an immediate AI-predicted performance response in seconds, with no re-meshing and no solver queue, plus automated sub-model generation for local design iteration and generative topology creation at the mesh level for exploring new part concepts without returning to CAD.

Agentic-AI uses autonomous agents to orchestrate multi-step CAE workflows - queuing solvers, monitoring convergence, running optimization loops, synchronizing variables across disciplines, and auto-generating 3D-embedded reports - without requiring constant engineer supervision. In AIWorks, an engineer describes a task in plain-English natural language, a Planner Agent builds a structured task plan, and CAD, Meshing, Solver, and Post-processing Agents execute it, intelligently routing each task between physics solvers and trained AI surrogate models based on runtime requirements and each model's Equivalence Score (Q8). This is what enables capabilities like autonomous overnight optimization loops across crash, NVH, stiffness, and CFD simultaneously - automating orchestration of the same MDO and ROM workflows described in Part 1, rather than replacing them, with the engineer remaining in control as the agent acts in a co-pilot capacity rather than a fully autonomous decision-maker.

MeshWorks provides the core CAE engineering - meshing, morphing, parametrization, ROM, and MDO - that aerospace and defence programs have relied on for structural, CFD, and durability validation, including DEP's documented bulkhead composite conversion, aircraft stabilizer weight optimization, and breech mechanism/motor torque investigation. AIWorks sits on top of that same foundation as the AI layer: parametric changes, morphing, and concept modelling generate a large population of geometry and thickness variants at mesh level without CAD rework, ROM runs that population fast enough to build a usable training set, the patented voxelisation engine statistically maps the resulting design space, and PINN training then produces a matched predictive model, generative model, and Equivalence Scorer for each load case - the same pipeline that has generated 250-plus structural variants from a single baseline model in other verticals. For aerospace and defence specifically, DEP lists structural analysis, CFD, fatigue, flight-path ROM, and composite optimization as deployed AIWorks application areas, and cites proof-of-concept engagements running in under six weeks once a program connects its existing solver. DEP also holds a US patent for its CAD-morphing technology, which underpins the morphing capability shared across both platforms.