Dynamic Solution#

The NeurEco Dynamic solution creates neural network models for time-series data where inputs and outputs evolve over time. These models capture the temporal dynamics of systems, making them ideal for simulation, prediction, and digital twin applications.

On this page

Overview#

Dynamic models learn to predict how a system responds over time to changing inputs (excitations). Unlike static models that produce instantaneous outputs, dynamic models maintain internal state and produce trajectories that evolve according to learned dynamics.

Key characteristics:

  • Models systems with memory (current output depends on past inputs/outputs)

  • Handles variable-length time series

  • Supports multiple experiments/sequences for training

  • Exports to FMU format for simulation tool integration

When to Use Dynamic#

Choose the Dynamic solution when:

  • Your data involves time-dependent behavior

  • Outputs depend on the history of inputs, not just current values

  • You’re modeling physical systems (mechanical, thermal, electrical, etc.)

  • You need to simulate system response to new input trajectories

  • You want to create a digital twin of a real-world system

Common applications:

  • Mechanical system simulation (vibrations, dynamics)

  • Thermal modeling (heat transfer, temperature prediction)

  • Control system design and testing

  • Process modeling in manufacturing

  • Environmental and climate modeling

Templates#

The Dynamic solution offers two templates for different types of temporal modeling:

Template

Best For

Key Features

ODE

Systems governed by differential equations, physical systems with continuous dynamics

Learns hidden states, supports steady-state constraints, initialization from known conditions

Convolutional Dynamic

Input-output relationships with time delays, signal processing, systems without explicit state

Sliding window approach, simpler architecture, faster training

Choosing Between ODE and Convolutional Dynamic#

Choose ODE when:

  • The system has well-defined physical states (position, velocity, temperature, etc.)

  • You need to initialize simulation from a known state

  • The system has continuous dynamics governed by differential equations

  • Long-term prediction accuracy is critical

Choose Convolutional Dynamic when:

  • You’re modeling input-output relationships with time delays

  • The system behavior can be captured by a finite time window

  • Training speed is a priority

  • You don’t need explicit state initialization

Data Requirements#

Dynamic models require time-series data organized as:

  • Time arrays: Time stamps for each sample. Convolutional Dynamic requires uniformly spaced time steps; ODE only requires the time stamps to be sorted in strictly increasing order (the spacing may be non-uniform).

  • Excitation (input) arrays: Input signals driving the system

  • Output arrays: Target system responses

Multiple sequences (experiments) can be used for training. For Convolutional Dynamic they must share the same, uniform time step; for ODE the durations and the spacing may differ from one sequence to another.

See Data Preparation for detailed formatting requirements.

Native File Format#

Dynamic models use the .ernn extension for the native NeurEco format. This format:

  • Preserves all model information

  • Allows continued work in NeurEco

  • Supports loading in the Python API

  • Can be re-exported to FMU format

Dynamic Solution