neural network dynamics models

**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.