What's Inside This Document
Next-Generation Model-Based Design Platform: Discover how Dyad (formerly JuliaSim) is revolutionizing engineering simulation by combining traditional physics-based modeling with cutting-edge Scientific Machine Learning (SciML). This platform overview reveals how engineers achieve up to 500x simulation acceleration while building more accurate digital twins that predict better from less data.
Revolutionary Performance & Capabilities
Unprecedented Speed Improvements
Up to 500x simulation acceleration compared to traditional tools
10x to 1000x faster than competing products
Seamless GPU integration for massive parallel computing
Cloud-based scalability for unlimited computational resources
Machine Learning Done Right for Engineers
Science + ML integration that combines physical knowledge with data
Automated physics discovery using advanced SciML techniques
Neural surrogates for ultra-fast simulation acceleration
Deep learned controllers for real-world deployment
What Makes JuliaSim Different
Complete Digital Twin Lifecycle
Learn how one unified platform handles your entire modeling workflow:
CALIBRATE - Automatically fit models to real-world data
DISCOVER - Use ML to find missing physics and higher-order effects
SURROGATIZE - Generate neural surrogates for instant simulation
CONTROL - Deploy intelligent controllers to embedded devices
Built on Proven Open-Source Foundation
Explore the robust technology stack:
Julia Programming Language - High-performance scientific computing
ModelingToolkit.jl - Advanced symbolic modeling framework
SciML Ecosystem - Cutting-edge scientific machine learning tools
Cloud-Native Architecture - Modern, scalable deployment
Comprehensive Domain Libraries
Pre-Built Industry Solutions
✅ HVAC Systems - Complete thermal and fluid modeling
✅ Battery Technology - Electrochemical and thermal simulation
✅ Multibody Dynamics - Mechanical system modeling
✅ Standard Library - Foundation components for any application
Advanced Modeling Capabilities
✅ Acausal Modeling - Build realistic models in minutes
✅ Equation-Based Digital Twins - Physics-first approach
✅ Multi-Physics Integration - Seamless domain coupling
✅ GPU Acceleration - Leverage modern hardware
Why Engineers Choose JuliaSim
Simple Yet Powerful
Pre-made component models accelerate development
Acausal modeling approach simplifies complex system design
Drag-and-drop interface for rapid prototyping
Realistic models built in minutes not months
Safe AI Integration
Uncertainty quantification for reliable predictions
Numerical stability ensuring robust simulations
Robust controls for real-world deployment
Physics-informed ML that respects fundamental laws
Precision That Delivers Results
Faster production cycles through accurate modeling
Lower development costs via reduced physical testing
Better predictions from less data using physics + ML
Real-world validation with embedded deployment
Perfect for Engineering Teams in
Aerospace & Defense - Flight dynamics and control systems
Automotive - Powertrain, thermal, and battery management
Energy - Power systems and renewable integration
Manufacturing - Process optimization and control
Robotics - Multibody dynamics and control design
Key Technology Differentiators
Scientific Machine Learning Integration
Unlike traditional tools that bolt ML onto existing platforms, JuliaSim integrates SciML from the ground up:
Physics-informed neural networks that respect conservation laws
Automatic differentiation for gradient-based optimization
Universal differential equations for hybrid modeling
Symbolic-numeric computation for optimal performance
Modern Software Engineering
Cloud-native deployment with elastic scaling
Version control integration for collaborative development
API-first architecture for seamless integration
Continuous deployment pipelines for rapid iteration
Ready to experience next-generation modeling and simulation? This document provides the complete overview of how JuliaSim transforms traditional engineering workflows with intelligent AI integration.
