Design Principle

Core design philosophy - Fully differentiable physical digital twin

Core Philosophy #

Key Principle

We aim to make all parameters definable and differentiable.

WiTwin is designed as a physical digital twin system where every parameter can be precisely controlled and optimized through automatic differentiation, just like neural networks, but grounded in physical reality.

Hierarchical Architecture #

The system follows a hierarchical structure that organizes all parameters:

Level	Description	Example
Scene	Top-level container	The entire simulation environment
Objects	Individual entities	Building, Radar, Antenna
Components	Functional modules	Transform, Material, Mesh
Fields	Actual parameters	position, rotation, frequency

Tip

Scene contains Objects, each object has Components, and each component has Fields (the actual parameters).

Parameter Space #

All scene parameters = Union of all object component fields

The entire parameter space of a scene is the collection of all fields from all components of all objects. This is analogous to the parameter space of a neural network, but instead of weights and biases, we have physical quantities like positions, materials, and RF properties.

Fusion of Differentiable DT and Neural Networks #

Neural Network Integration

A key design principle: Parameters can be transformed into deep learning models.

Our system allows certain parameters to be replaced by neural networks. Instead of fixed values, parameters can be computed by neural network models, enabling learned behaviors and adaptive systems.

Key capabilities:

Hybrid Modeling: Combine physics-based simulation with learned components
Parameter Networks: Neural networks that predict material properties, beam patterns, or propagation coefficients
End-to-End Differentiability: Gradients flow through both physical and neural components
Unified Optimization: Jointly optimize physical parameters and neural network weights

Hyperparameters System #

In practice, we don't always need to control or optimize every individual parameter. Some parameters may be related, or we may want to control groups of parameters through higher-level abstractions.

What are Hyperparameters?#

Hyperparameters are higher-level variables that control one or more scene parameters through computational relationships. They provide:

Abstraction: Control multiple related parameters with a single variable
Constraints: Enforce physical relationships between parameters
Optimization: Reduce the optimization space for inverse problems
Differentiation: Enable gradient-based optimization through the differentiable toggle

Visual Node Editor #

The relationship between hyperparameters and component fields is defined through the visual node editor:

Hyperparameter Nodes: Input nodes representing controllable variables
Field Nodes: Output nodes representing object component fields
Operation Nodes: Mathematical operations connecting hyperparameters to fields
Gradient Flow: When differentiable is enabled, gradients flow through the node graph

Bidirectional Synchronization #

A key design principle is the real-time, bidirectional synchronization between the UI editor and Python code.

Changes made in Python code are immediately reflected in the UI editor:

# Change in Python
cube.transform.position = [5, 0, 0]
 
# Viewport updates immediately
# Properties panel shows new values
# No manual refresh needed

Headless Mode #

Warning

The UI editor is not required.

Python code can run completely independently in headless mode without any UI.

This is essential for:

Batch Processing: Running large-scale simulations on servers
Optimization: Automated parameter sweeps and gradient-based optimization
Integration: Embedding in existing pipelines and workflows
Deployment: Production systems without UI dependencies

from witwin import Scene, Server
 
# Headless mode - no UI
scene = Scene()
server = Server(scene=scene)
server.start(open_browser=False)
 
# Full simulation capabilities
# All parameters accessible
# Differentiable computations
# No browser required