NEURAL SURROGATES PINNS & REDUCED-ORDER MODELS

AI-Driven Physics & Surrogate Modeling

Training physics-informed neural surrogates to deliver 1,000x to 10,000x acceleration over traditional 3D numerical solvers without compromising governing conservation laws.

Request AI/ML Physics Scope

COMPUTATIONAL ACCELERATION

Bypassing the Multi-Day Simulation Bottleneck

Evaluating thousands of design iterations or running real-time operational digital twins using full 3D transient Navier-Stokes or non-linear FEA is computationally intractable.

We build Physics-Informed Neural Networks (PINNs) and Proper Orthogonal Decomposition (POD) surrogate models. By embedding partial differential equation (PDE) residuals directly into neural network loss functions, our surrogates evaluate complex flow and stress fields in milliseconds.

Loss Formulations with PDE Residuals Proper Orthogonal Decomposition (POD) Kriging & Gaussian Process Regressors Real-time Digital Twin Inference

SURROGATE BENCHMARKS

Inference Acceleration 1000x – 10,000x Speedup vs. Full 3D CFD/FEA

Residual L2 Error < 1.5% Across Validated Parameter Space

Model Footprint Embedded Edge Execution (< 50MB Binary)

Training Efficiency Sparse Sampling via Latin Hypercube DOE

APPLICATIONS

Industry Scenarios

→ Sub-second aerodynamic prediction for generative styling studios
→ Real-time industrial boiler thermal monitoring via digital twins
→ Predictive maintenance remaining useful life (RUL) estimation
→ Battery degradation prognosis under stochastic duty cycles

DELIVERABLES

What You Receive

✓ Exportable ONNX / FMU functional mock-up units for 1D systems
✓ Trained neural surrogate weights with automated error bounds
✓ Interactive web-based engineering exploration dashboards
✓ Full training corpus and Latin hypercube sampling manifests

Deploy Real-Time Physics Surrogates for Your Products

Accelerate parametric optimization and build operational digital twins with our computational AI specialists in Hyderabad.

Explore AI Surrogate Modeling