MWORKS Syslab Online
Browser-based scientific computing with Julia. No installation required.
|Try It Yourself
Run Julia code directly in your browser. No installation required.
Visualize how small changes in initial conditions lead to unpredictable outcomes.
# Lorenz System - Sensitive Dependence
using MWorks, Plots, DifferentialEquations
function lorenz!(du, u, p, t)
σ, ρ, β = p
du[1] = σ * (u[2] - u[1])
du[2] = u[1] * (ρ - u[3]) - u[2]
du[3] = u[1] * u[2] - β * u[3]
end
u0 = [1.0; 0.0; 0.0]
p = [10.0, 28.0, 8/3]
tspan = (0.0, 50.0)
prob = ODEProblem(lorenz!, u0, tspan, p)
sol = solve(prob)
plot(sol, vars=(1,2,3), lw=0.5)
title!("Lorenz Attractor")Signal Processing & Communications
Analyze sampled signals, design digital filters, and visualize frequency content.
Automation & Control Systems
Model dynamic systems, design controllers, and deploy via auto-generated code.
Data Science & AI
Perform clustering, classification, and train neural networks for predictive modeling.
Image Processing & Vision
Segment, filter, and enhance images. Build computer vision pipelines.
Built for Engineers & Scientists
General-Purpose Programming & Algorithms
- Interactive Julia development environment
- Python and M language compatibility
- Cross-language integration (C/C++, Fortran)
- Full debugging and profiling support
High-Performance Math Engine
- Symbolic computation & curve fitting
- Optimization & global optimization
- Concise expression for complex problems
- JIT-compiled for maximum efficiency
Data Analysis & Visualization
- Import MAT, CSV, TXT, Excel, HDF5, JSON
- Data preprocessing & statistical analysis
- Publication-ready scientific graphics
- Customizable annotations & tooltips
Professional Toolboxes
- Signal processing & wireless communications
- Control systems toolbox
- AI & data science toolkit
- Extensible platform architecture
Three Simple Steps
Write Julia Code
Use familiar Julia syntax with MWorks extensions for modeling and simulation.
using MWorks
model = System()Run Simulation
Execute your model with high-performance solvers optimized for engineering problems.
result = solve(model)
plot(result)Analyze Results
Visualize and export results for further analysis or integration with other tools.
export(result, "csv")
generate_report()Learn by Example
Explore pre-built examples across different engineering domains.
Quarter-Car Suspension Model
Predict vehicle ride comfort by simulating how the suspension responds to road bumps.
Heat Sink Cooling Analysis
Determine if an electronic enclosure will overheat under continuous load.
DC Motor Speed Controller
Tune a PID controller to make a motor reach target speed without overshooting.
Brake Pedal Optimization
Find the lightest bracket design that still meets minimum strength requirements.
Sensor Noise Filtering
Remove electrical noise from accelerometer readings to get clean motion data.
Project Schedule Risk
Estimate how likely a project is to finish late given uncertain task durations.
Ready to Start?
Begin your journey with MWORKS Syslab. No installation, no setup, just pure computing power in your browser.