neural networks · surrogate modelling · hybrid solvers
What We Do
Deep Learning is a subset of machine learning where artificial neural networks learn from large amounts of data. Integrating deep learning with CAE enables rapid approximation of complex simulation results, real-time inference, and hybrid workflows that combine data-driven components with physics solvers to improve accuracy and efficiency.
Key Benefits
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neural networks · surrogate modelling · hybrid solvers
Deep Learning is a subset of machine learning where artificial neural networks learn from large amounts of data. Integrating deep learning with CAE enables rapid approximation of complex simulation results, real-time inference, and hybrid workflows that combine data-driven components with physics solvers to improve accuracy and efficiency.
Key Aspects
Train neural networks on high-fidelity simulation data to create fast surrogate models that predict key outcomes with low latency for design iterations and optimization.
Combine traditional physics-based solvers with learned components to accelerate convergence, reduce mesh requirements, or replace expensive sub-model evaluations.
Use probabilistic networks and ensemble techniques to estimate prediction confidence and integrate uncertainty into downstream decision-making and design tradeoffs.
Discuss how deep learning can accelerate your simulation workflows and reduce engineering cycle time.
