Surrogate Modeling & Reduced-Order Models (ROMs)

We use Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Bayesian Optimization to optimize designs and explore parameter spaces efficiently. Topology Optimization: ▸ GANs/VAEs generate …

We propagate uncertainties in material properties, boundary conditions, and operational parameters through ML models to quantify confidence intervals for FEA/CFD outputs. Stochastic PINNs: ▸ Extend …

We develop dynamic ROMs using Recurrent Neural Networks (RNNs) or Long Short-Term Memory (LSTM) networks to predict real-time system degradation. Dynamic ROMs: ▸ Trained on …

We validate ML predictions against experimental data and benchmark results to ensure accuracy and reliability. Residual Learning: ▸ ML models trained on solver residuals correct …

• Rigorous ML Architectures: Prioritize interpretability via attention mechanisms, explainable AI (XAI), and embedded conservation laws. • HPC Scalability: Deploy distributed training (Horovod, Ray) for …

Our AI/ML-integrated FEA services leverage machine learning to optimize structural simulations, automate design modifications, and predict stress/strain with high fidelity—reducing solve times by 40-70% while …

Our AI/ML-integrated CFD services accelerate fluid flow and heat transfer analysis by 8-12x while maintaining >99% correlation with high-fidelity simulations, enabling faster design cycles without …

We hardwire fundamental physics into AI/ML models through governing PDEs, achieving <2% deviation from experimental benchmarks while accelerating simulations 10-50x compared to traditional methods. Navier-Stokes …

Physics-Informed Neural Networks (PINNs): ▸ Embed governing Partial Differential Equations (PDEs) as loss functions during ML training ▸ Ensures predictions respect physical laws (Navier-Stokes for …