Modern engineering teams can't afford 20-minute FEA runs for every design variant. We train ML surrogates on your simulation data so exploration becomes interactive.
What we do
YantriX builds physics-informed neural networks (PINNs) and ML surrogates trained on FEA / CFD / multi-physics runs, letting engineers explore thousands of design variants in real time. We also deliver generative-design workflows (topology optimization + ML ranking), digital-twin systems that fuse sensor telemetry with simulation, and predictive-maintenance models over vibration, thermal, and current-signature data.
Share your technical requirements, 3D CAD files, or operating specs. NDA support is available where required before confidential file exchange.
We adapt the same engineering service to different product contexts depending on the load case, packaging problem, validation target, or deployment environment.
Tailored engineering, mechanical design, and rapid prototyping solutions designed specifically for industrial equipment applications.
Tailored engineering, mechanical design, and rapid prototyping solutions designed specifically for automotive applications.
Tailored engineering, mechanical design, and rapid prototyping solutions designed specifically for aerospace and drones applications.
Tailored engineering, mechanical design, and rapid prototyping solutions designed specifically for energy and solar applications.
Tailored engineering, mechanical design, and rapid prototyping solutions designed specifically for robotics applications.
Tailored engineering, mechanical design, and rapid prototyping solutions designed specifically for consumer hardware applications.
See how this engineering capability applies across real project scopes, analysis goals, and physical prototype iterations.
ML-accelerated engineering
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.
Structural analysis
How simulation-driven design optimization cut peak stress by 32% and deformation by 25% on a load-bearing industrial bracket — before any physical prototype was built.
Explore practical engineering guides related to design, manufacturing, simulation, and product development.
ML-Accelerated Engineering
What PINNs actually are, where they beat classical FEA, where they don't, and how to integrate them with SolidWorks and ANSYS workflows — without the academic-paper handwaving.
ML-Accelerated Engineering
How to build an ML surrogate model for FEA / CFD in 2026 — training data design, model architecture, validity envelopes, and integration with SolidWorks for interactive design exploration.
Simulation
How CFD thermal analysis with Conjugate Heat Transfer (CHT) catches hotspots, optimizes heatsink fins, and balances IP67 sealing with heat dissipation in electronics enclosures.
Common questions teams ask before starting a project.
They can — which is why we care about the training-data design more than the model choice. We treat simulation as an active-learning problem: choose the samples that matter, validate against held-out physics, and publish the validity envelope so downstream users know when to fall back to the full solver.
It depends on dimensionality and how smooth the response surface is. For narrow parametric studies, a few hundred runs can produce a useful surrogate. For broader design spaces we scope the data collection as the first phase.
Yes. We typically run training jobs against your native solver (ANSYS, SolidWorks Simulation, OpenFOAM) and serve the surrogate behind a REST or gRPC API that plugs into your existing CAD / design tooling.
Share your target latency, hardware constraints (Jetson / ESP32), and data availability. We build models and firmware that run reliably in the field.