PyLCSS: Low-Code System Solutions

Overview

https://github.com/kutaydemir07/PyLCSS PyLCSS (Python Low-Code System Solutions) is a high-performance engineering platform designed to bridge the gap between intuitive visual design and rigorous mathematical analysis

It enables engineers to model complex systems using a node-based interface, explore high-dimensional Solution Spaces, and optimize designs using industry-standard algorithms. Built for robustness, it features a crash-free multi-threaded architecture, vectorized computation kernels, and integrated AI capabilities.

1of3

Scientific Foundation: Solution Spaces

PyLCSS implements the Solution Space approach for robust design. Instead of seeking a single optimal point (which may be sensitive to manufacturing tolerances), PyLCSS identifies box-shaped regions of valid designs. This allows for decoupled development of subsystems in complex engineering projects.

Reference Algorithm: > The solution space computation methods are based on:
Markus Zimmermann, Johannes Edler von Hoessle, “Computing solution spaces for robust design”, International Journal for Numerical Methods in Engineering, 2013.
DOI: 10.1002/nme.4450

Key Features

Visual Modeling Environment

Node-Based Architecture: Intuitive drag-and-drop interface powered by NodeGraphQt.
Unit Intelligence: Automatic dimensional analysis and compatibility checking via Pint ensures physical consistency.
Python Integration: Write custom logic blocks with full NumPy support.
CAD Modeling: Parametric 3D CAD design using CadQuery with node-based workflow.

Advanced Analysis Suite

Monte Carlo Exploration: Rapidly evaluate thousands of design variants using vectorized sampling.
Solution Space Visualization: Interactive 2D scatter plots, parallel coordinates, and feasibility maps.
Global Sensitivity Analysis: Variance-based Sobol indices (via SALib) to identify critical design drivers.
FEM Simulation: Finite element analysis with scikit-fem and Netgen meshing for structural analysis.

AI & Optimization

Surrogate Modeling: Replace expensive simulations with fast approximations using PyTorch Neural Networks, Random Forests, or Gradient Boosting.
Multi-Objective Optimization: Generate Pareto fronts using state-of-the-art solvers:
- Gradient-Based: SLSQP (SciPy)
- Gradient-Free: Nevergrad, Differential Evolution, COBYLA

Industrial-Grade Performance

Vectorized Kernels: Calculation engines are optimized with NumPy vectorization for maximum throughput.
Non-Blocking UI: Heavy computations run in background threads with signal throttling to ensure the GUI remains responsive at 60 FPS.
Crash Protection: Robust error handling and race-condition prevention using Mutex locks.

LLM-Powered Voice Assistant

Natural Language Control: Speak naturally to control the UI (e.g., “Zoom in”, “Go to properties”).
AI Coding Assistant: Ask the LLM to generate complex systems (e.g., “Create a helical gear”).
Local Speed: Uses Faster-Whisper for real-time local speech recognition.
Privacy-First: Supports multiple LLM providers (OpenAI, Claude, Gemini, LM-Studio) with optional localized execution.
Hands-Free: Full voice command suite for when your hands are busy with hardware or VR.

Installation

Prerequisites

Python: 3.8 or higher
OS: Windows 10/11, macOS, or Linux

Quick Install

# 1. Clone the repository
git clone <repository-url>
cd pylcss

# 2. Create and activate a virtual environment (Recommended)
python -m venv venv
# Windows:
venv\Scripts\activate
# Linux/Mac:
source venv/bin/activate

# 3. Install dependencies
pip install -r requirements.txt

# 4. Launch PyLCSS
python scripts/main.py

Quick Start Guide

Launch the App: Run run_gui.bat (Windows) or execute python scripts/main.py.

Load a Model: Navigate to File > Open and select data/Gear Unit.json.

Validate: Click the “Validate” button to check for unit consistency and connection errors.

Compute: Switch to the Solution Space tab and click “Compute” to generate design samples.

Visualize: Use the “Plot Settings” to visualize trade-offs between Weight vs Safety Factor.

Optimize: Go to the Optimization tab, select objectives (e.g., Minimize Weight), and run the solver.

Tech Stack

PyLCSS is built on the shoulders of giants:

UI/UX: PySide6, NodeGraphQt, QtAwesome

Computation: NumPy, SciPy, Pandas

Visualization: PyQtGraph

Machine Learning: PyTorch, Scikit-learn

Optimization: Nevergrad, SALib

Physics: Pint

CAD and FEM: CadQuery, VTK, scikit-fem, Netgen, meshio

License

PyLCSS is licensed under the PolyForm Shield License 1.0.0.

Allowed: Personal use, academic research, internal business use.

Restricted: You cannot use this software to build a competing product or service.

See LICENSE for full details.