PyTorch2LTspice

PyTorch2LTspice enables the integration of neural networks into circuit simulations by converting PyTorch models into LTspice-compatible behavioral subcircuits (.subckt).
This tool allows deep learning inference—including supervised learning models and reinforcement learning policies—to run directly within LTspice.

By combining with the LTspicePowerSim environment, users can evaluate neural network behavior across a wide range of power electronics applications, such as DC-DC converters, inverters, and motor drivers.

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📌 Features

• Converts PyTorch nn.Sequential models to LTspice-compatible .subckt format
• Supports:
  • nn.Linear
  • nn.ReLU
  • nn.Sigmoid
• Outputs a netlist using behavioral voltage sources (B elements)
• Auto-generates LTspice node names (NNIN1, NNIN2, ..., NNOUT1)
• Easy integration into LTspice testbenches

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🧠 Motivation

Neural networks trained in Python (with PyTorch) can now be exported and tested directly in LTspice circuit simulations.
This allows for:

• Closed-loop simulation with NN controllers
• Verification of inference logic inside switching power supplies
• Observation of behavior under nonlinear and dynamic conditions

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✨ Example

NN Controlled Voltage Mode Buck (WIP)

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🚀 How to Use

1. Define a model in PyTorch

import torch.nn as nn

model = nn.Sequential(
    nn.Linear(20, 32),
    nn.ReLU(),
    nn.Linear(32, 16),
    nn.ReLU(),
    nn.Linear(16, 1)
)
model.eval()

2. Export as LTspice .subckt file

from PyTorch2LTspice import export_model_to_ltspice

export_model_to_ltspice(
    model,
    filename="TEST_MODEL_SUBCKT.SP",
    subckt_name="TESTACTORSUBCKT"
)

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📂 Output Example

The resulting LTspice subcircuit will look like:

.SUBCKT TESTACTORSUBCKT NNIN1 NNIN2 ... NNIN20 NNOUT1
* LAYER 1: LINEAR
B1_1 L1_1 0 V=V(NNIN1)*(-0.179081)+V(NNIN2)*(-0.068428)+...
...
* ACTIVATION LAYER 1: RELU
B_ACT1_1 L_ACT1_1 0 V=(IF(V(L1_1)>0,V(L1_1),0))
...
B_OUT NNOUT1 0 V=V(L_ACT2_1)
.ENDS TESTACTORSUBCKT

You can include it in your LTspice schematic with .INCLUDE TEST_MODEL_SUBCKT.SP, and wire it to your simulated environment.

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📎 Dependencies

• Python 3.7+
• PyTorch
• NumPy

Install with:

pip install torch numpy

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📄 License

MIT License

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🧩 Related Projects

• 🔗 LTspicePowerSim:
  A Simulink-like power electronics simulation environment built on LTspice,