Aero/Drag

Crash/Side Impact

NVH Trimmed Body

Globally Completed

All accuracy figures validated against real customer simulation data - not benchmark datasets. Equivalence scoring provides per-prediction confidence before any AI result is used in a design decision.

Physics-Informed Neural Networks (PINN) replace costly solver runs. Trained per load case with physics equations - Navier-Stokes, finite element equations, and LES - embedded directly in the training loss function. Predicts scalars, time-history curves, and full 3D field contours. Delivers <3% prediction error on aerodynamic drag and body stiffness, and 95%+ R² on highly non-linear crash responses. An equivalence score validates AI model confidence before any prediction is used in a design decision.

Machine intelligence for geometry feature recognition across complex assemblies - automatically identifying ribs, bosses, fillets, spot welds, MIG welds, holes, and flanges directly on finite element models. Converts recognised features into smart parametric handles, enabling rapid new design creation and AI-assisted concept generation without returning to CAD. Drives mass training-data creation for downstream AI workflows. Supports casting, stamping, injection moulding, and welded assembly geometries.

AI-driven generative geometry meets instant re-prediction. Modify a CAD shape through morphing - change a spoiler angle, roof curvature, or panel profile — and receive an immediate AI-predicted performance response, with no re-meshing and no solver queue. Results in seconds. Includes automated sub-model generation for local design iteration, generative topology creation at mesh level (new parts without returning to CAD), and Digital Twin creation by mapping manufactured scan data - including geometry, thickness variation, pre-strain, and distortion - onto nominal FE models for true as-built simulation.

Autonomous simulation agents that orchestrate complete, multi-step CAE workflows without constant engineer supervision. Agentic AI intelligently routes tasks between physics solvers and AI surrogate models based on runtime requirements and the availability of trained AI models. Capabilities include multi-solver orchestration, autonomous overnight optimisation loops, multi-discipline variable synchronisation across crash, NVH, stiffness, and CFD, and auto-generation of 3D-embedded reports on workflow completion. The engineer remains in control and Agentic AI acts as a co-pilot.

Every figure benchmarked against production solver runs - not curated datasets.

Multiple body configurations . full-vehicle external flow

Prediction accuracy vs. solver baseline

BIW load cases . multiple vehicle architectures (90+ variants)

Prediction accuracy vs. solver baseline

B-pillar acceleration time history . multiple load paths

Correlation coefficient vs. full solver run

Multiple wheelbases roof configurations. trimmed body

Frequency prediction vs. solver baseline

Generate hundreds of simulation-ready variants from one baseline - without returning to CAD

• Parametric FE changes, morphing, and concept modelling create diverse geometry, thickness, and material variants at mesh level.

• ROM runs the variant population fast - compressing solver time from days to hours - feeding a rich AI training dataset.

• Every AI model is only as good as its training data - most organisations don't have enough of it.

• Other platforms assume the data pipeline is already solved. AIWorks builds it.

  
  
  

250+ Crash models from 1 baseline vehicle - zero CAD rework.

Statistically map geometry, thickness, and results across hundreds of models - before training begins

• Patented Voxelation robotically scans all variants and places them in a common 3D space - geometry, thickness, material, and result data mapped statistically.

• Surfaces cause-vs-effect design intelligence across the full population - inputs and outputs - without loading individual files into memory.

• Loading 100 solver models conventionally is impossible

• AI model quality is determined here - before a single training epoch runs.

  
  
  

The only platform with a statistical design-space analysis engine as a native pre-training step.

Physics embedded in the training loss - FE equations, Navier-Stokes, LES - not bolted on after the fact

• Separate neural network architectures per problem class - mildly, moderately, and highly nonlinear - each with matched physics equations in the loss.

• Adaptive per load case. Equivalence scoring calibrated during training to set the model's confidence boundary before deployment.

• Standard ML extrapolates blindly beyond training data - a silent failure in engineering simulation.

• Physics in the loss forces the network to respect governing equations, enabling accurate extrapolation on new geometries.

Three problem-class architectures - mildly, moderately, highly nonlinear - each physics-tuned.

Three models per load case - predictive, generative, and a confidence scorer - from a single training run

• Predictive AI replaces the solver. Generative AI works like a topology optimizer. Equivalence Scorer rates prediction confidence before use.

• All three produced automatically per load case - high score means act on it, low score flags the result and invokes the solver.

• Prediction tells you what. Generative tells you how. The scorer tells you whether to trust it - all three are needed to act on AI results safely.

• Without the scorer, there is no signal when a design has drifted outside the reliable prediction envelope.

The Equivalence Scorer - the trust mechanism no competing platform provides - tells engineers exactly when to rely on AI and when to invoke the solver.

Stress, frequency, drag, displacement - scalars, curves, and full 3D fields - in seconds, not days

• Predicts scalar, vector, and full 3D field responses - stress, pressure, frequency, displacement - for any new design without a solver run.

• Equivalence score runs per prediction - AI surrogate or physics solver invoked automatically based on confidence level.

• AI prediction removes the solver queue as the design bottleneck.

• Computational cost reduced by orders of magnitude - without sacrificing engineering rigour.

<1% drag error. Full 3D pressure field, not just scalar CD. 95%+ R² on crash model solver time-history. Highly nonlinear side impact.

MDO across crash, stiffness, NVH, CHT, and CFD simultaneously - AI or physics solver, chosen automatically

• Parametric variables linked directly to the MDO loop - crash, stiffness, NVH, CHT, and CFD driven simultaneously in one run.

• Agentic AI executes overnight loops - monitors, switches between AI and solver per response, and auto-reports on completion.

• Sequential single-discipline optimisation misses trade-offs - the best crash design is rarely the best NVH design.

• Simultaneous MDO with automatic AI-vs-solver switching is what makes multi-discipline optimisation feasible at engineering scale.

Built-in MDO - Crash · NVH · CFD · Stiffness · CHT in one loop, inside AIWorks.

Every AI surrogate model - regardless of the platform it is trained on - is only as good as the data behind it. Most engineering organisations, even large OEMs, do not have hundreds of labelled simulation variants organised and ready for AI training across every load case. AIWorks solves this at the source. Using DEP's parametric CAE, morphing, concept modelling, and Reduced Order Modelling (ROM) capabilities - integrated within the MeshWorks platform - you can generate hundreds of simulation-ready variants from a single baseline model, without returning to CAD. The ROM engine then runs those variants fast, compressing days of solver time into hours. The patented Voxelation engine analyses the resulting design population statistically before AI training begins. Data generation → statistical analysis → AI training → deployment. All in one platform.

Single FE/ CFD

Parametric + morphing

No CAD rework

Per load case

Structural, crash, durability, and fatigue specialists can replace multi-day solver runs with sub-minute AI predictions across multiple load cases simultaneously.

Aerodynamics, CHT, and cabin comfort engineers can do drag prediction in 2 minutes vs. 3-week CFD queue. 3D pressure field results generated on demand for new styling geometries.

Engineering managers can build AI-ready CAE organizations - from data strategy and model governance to agentic workflow deployment at scale across multiple product lines.

Product designers can use GEOM AI to morph geometries and re-predict performance in real time - closing the loop between styling and simulation on the same day.

Trimmed body frequency optimization, acoustic performance improvement achieved with - AI-predicted natural frequencies at sub-5% error across wheelbases and roof configurations.

Data scientists applying ML to physical simulation data can leverage DEP's pre-trained PINN architectures tuned specifically for mildly, moderately, and highly non-linear engineering problems.