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.
We adapt the same engineering service to different product contexts depending on the load case, packaging problem, validation target, or deployment environment.
Relevant when the project needs focused ml-accelerated engineering support.
Relevant when the project needs focused ml-accelerated engineering support.
Relevant when the project needs focused ml-accelerated engineering support.
Relevant when the project needs focused ml-accelerated engineering support.
Relevant when the project needs focused ml-accelerated engineering support.
Relevant when the project needs focused ml-accelerated engineering support.
These links help visitors move from service intent to real examples of engineering work.
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.
Technical articles give Google more paths into the service pages and help visitors explore adjacent engineering questions before they get in touch.
3D Printing
Learn how 3D printing services help startups and manufacturers in India validate CAD designs, reduce prototyping cost, and build functional parts faster.
Applied AI
Walkthrough of shipping a segmentation-class YOLOv11 model to a Jetson Orin Nano at production latency — quantization, TensorRT conversion, and the pitfalls.
Simulation
How CFD-based thermal analysis catches hotspots, airflow dead zones, and IP67-versus-cooling trade-offs in electronics enclosures before the first prototype ships.
Service-specific questions are useful for both users and search visibility around intent-driven queries.
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.
Send the problem, your current design stage, and any existing files. We can scope the work from there.