For an industrial-equipment client running hundreds of FEA sweeps per design cycle, YantriX built an ML surrogate that predicts stress fields in tens of milliseconds — making full topology and parameter exploration interactive instead of overnight.
Overview
A physics-informed neural network trained on 12,000 ANSYS runs replaces the full solver for early-stage topology — predicts stress fields in 40 ms vs. 22-minute solves.
Engagement: Engineering Study
Discipline: Simulation & Validation
Project Type: Applied AI + Engineering Simulation
Application: Industrial equipment
Key specifications
Objectives
Challenge
The client’s design team was running ANSYS studies to validate bracket and frame variants. Each solve took 18-22 minutes, which capped the number of variants the team could realistically explore before freezing geometry. They asked whether ML could give them an interactive stress-field preview inside their parametric CAD workflow.
Approach
Defined the parameter space (thickness distribution, rib count, fillet radii, load direction) and ran an active-learning campaign to pick the 12,000 ANSYS runs that spanned it most efficiently.
Trained a physics-informed neural network over stress-field outputs, using the mesh topology as a graph structure and validating against held-out physics.
Deployed the model behind a FastAPI service that the SolidWorks task-pane add-in calls on every parameter change.
Published a validity envelope so engineers know when the surrogate is inside its trained regime versus when to fall back to ANSYS.
Outcomes
Deliverables
Tools used
Impact
Conclusion
The real value isn’t replacing FEA — it’s making exploration cheap enough that engineers actually do it. The full solver remains the source of truth; the surrogate just makes the path there much faster.
Working on a similar engineering problem?
Running a lot of similar FEA / CFD studies? Let’s talk about whether a surrogate fits — and what the training campaign would look like.
Tagged
Frequently asked questions
No — it accelerates the inner loop of design exploration. ANSYS stays the source of truth for novel geometries and final certification. The surrogate predicts in milliseconds inside its trained envelope; outside the envelope it refuses to predict and engineers fall back to the full solver.
With active learning, far less than uniform sampling. For this engagement we hit 0.987 R-squared on held-out geometries with ~12,000 ANSYS runs versus an estimated 80,000+ needed for uniform sampling. The trick is in the data-selection campaign, not the model architecture.
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