Neural Network Dynamics Models are data-driven models that use neural networks to learn the dynamics of physical or manufacturing systems — replacing first-principles equations with learned representations that can capture complex, nonlinear behavior from process data.

What Are NN Dynamics Models?

• Input: Current state + control inputs -> Output: Next state (discrete-time) or state derivative (continuous-time).
• Architectures: Feedforward NNs, RNNs/LSTMs (for temporal dynamics), Physics-Informed NNs (PINNs).
• Training: Learn from historical process data or simulation data.

Why It Matters

• Process Control: Provides the internal model for MPC when first-principles models are unavailable or too complex.
• Digital Twins: Forms the core prediction engine in digital twin frameworks for semiconductor equipment.
• Flexibility: Can model systems with unknown physics, high dimensionality, or complex nonlinearities.

NN Dynamics Models are learned physics engines — neural networks trained to predict how a system evolves in time, enabling model-based control without manual equation derivation.