Modern Modeling and Simulation powered by ML

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JuliaSim is the next-generation, cloud-based platform for model-based design. Using modern scientific machine learning (SciML) techniques and equation-based digital twin modeling and simulation, JuliaSim accelerates simulation times, significantly reducing workflow runtime from months to hours. JuliaSim encompasses block diagrams, acausal modeling, state transition diagram and a differentiable programming language all within a single environment.

High-performance electrochemical lithium-ion battery simulations

Design Multiphysics Models - Faster, Better

Unbeatable combination of differentiability and symbolic manipulation

Seamless integration of graphical and text-based workflows with drag & drop model development

Component libraries across engineering domains (e.g. multibody, batteries, multi-phase fluids)

Sophisticated AI capabilities - Use of machine learning and real world data

Deterministic autocoding with traceability and reproducibility for regulatory requirements

Modern development workflows (git, CI/CD), significantly improved performance

Leverage the best of science and machine learning, digital twins and surrogates for acceleration

Proprietary solvers and methods combining science, physics, and data.
Design Multiphysics Models - Faster, better, with reproducibility and traceability.

Design and Calibrate

JuliaSim supports the creation of both the controller and plant models needed for system engineering.

Using machine learning and differentiability techniques, JuliaSim not only provides efficient model calibration leveraging real world data, but it can use the same real world data to help identify missing physics in your models.

Beyond Design

JuliaSim supports the entire product lifecycle. By collecting real world telemetry JuliaSim supports:

Operations: Use tuned models in the field to support optimizing operations

Diagnostics and Prognostics: Identify impending failures and their causes

Wear: Retrain physical component models to account for wear, aging, etc.

Leverage key benefits of JuliaSim across the product lifecycle

Product Architecture Development

Use JuliaSim to explore different architectures and choose the best initial architecture.

Development of Subsystems

Easily decompose systems into subsystems, managing variants, and adjusting fidelity for precise analysis with JuliaSim.

Development of Plant Models

Integrate physical system modeling, state transition systems, and controllers, allowing comprehensive analysis and deployment from a single tool.

Model Verification

Support for model verification by comparing high-level requirements and enabling system testing through MiL and HiL integration.

Model Deployment

JuliaSim supports model deployment with FMI v3 integration and upcoming C code generation capabilities for diverse industrial applications.

Field Operations

Enhance product value in the field through telemetry data analysis and various high-value operational use cases.

Model Calibration

Refine model parameters using real-world data, leveraging differentiability for precise model calibration.

Discovery of Missing Physics

JuliaSim employs embedded neural networks to identify and incorporate missing physical effects into models for better product insights.

Prediction & Diagnostics

JuliaSim optimizes product duty cycles and costs with reliable predictions on field behavior while using real-world data to diagnose field failures and inform future product designs for enhanced durability and resilience.

Computational Product Design - Case Study | JuliaHub

Williams Racing Unlocks SciML using JuliaSim

Teams are continually seeking ways to improve performance on and off the track. One key area of focus is the use of simulation to understand the performance of the car and make strategic decisions during races.

Case Study

Auto Crash Simulation

Instron Uses JuliaSim for 500x Faster Design Time, Leading to Faster New Product Design

Case Study

Circuitscape

Circuitscape.jl uses electrical circuit theory to model animal, plant and human migration patterns and their response to climate change

Case Study

JuliaSim = Science + ML

JuliaSim is machine learning done right for engineers. Mix scientific knowledge of physical and chemical processes with data to build digital twins that predict better from less data.

Learn More

LEARN MORE ABOUT JULIASIM AND HOW IT IS BEING USED

Modeling and Simulation with JuliaSim

Dr. Chris Rackauckas

WEBINAR

Modeling Battery Lifespan with JuliaSim

Dr. Marc Berliner

WEBINAR

SciML: Scientific Computing + Machine Learning = Industrial Modeling for Engineers

Dr. Chris Rackauckas

WEBINAR

Ready to see how JuliaSim can accelerate your product development? Speak to a member of our team.

Contact Sales

Modern Modeling and Simulation powered by ML

High-performance electrochemical lithium-ion battery simulations

Design Multiphysics Models - Faster, Better

Unbeatable combination of differentiability and symbolic manipulation

Seamless integration of graphical and text-based workflows with drag & drop model development

Component libraries across engineering domains (e.g. multibody, batteries, multi-phase fluids)

Sophisticated AI capabilities - Use of machine learning and real world data

Deterministic autocoding with traceability and reproducibility for regulatory requirements

Modern development workflows (git, CI/CD), significantly improved performance

Leverage the best of science and machine learning, digital twins and surrogates for acceleration

Proprietary solvers and methods combining science, physics, and data.Design Multiphysics Models - Faster, better, with reproducibility and traceability.

Design and Calibrate

JuliaSim supports the creation of both the controller and plant models needed for system engineering.

Beyond Design

JuliaSim supports the entire product lifecycle. By collecting real world telemetry JuliaSim supports:

Leverage key benefits of JuliaSim across the product lifecycle

Product Architecture Development

Use JuliaSim to explore different architectures and choose the best initial architecture.

Development of Subsystems

Easily decompose systems into subsystems, managing variants, and adjusting fidelity for precise analysis with JuliaSim.

Development of Plant Models

Integrate physical system modeling, state transition systems, and controllers, allowing comprehensive analysis and deployment from a single tool.

Model Verification

Support for model verification by comparing high-level requirements and enabling system testing through MiL and HiL integration.

Model Deployment

JuliaSim supports model deployment with FMI v3 integration and upcoming C code generation capabilities for diverse industrial applications.

Field Operations

Enhance product value in the field through telemetry data analysis and various high-value operational use cases.

Model Calibration

Refine model parameters using real-world data, leveraging differentiability for precise model calibration.

Discovery of Missing Physics

JuliaSim employs embedded neural networks to identify and incorporate missing physical effects into models for better product insights.

Prediction & Diagnostics

JuliaSim optimizes product duty cycles and costs with reliable predictions on field behavior while using real-world data to diagnose field failures and inform future product designs for enhanced durability and resilience.

Williams Racing Unlocks SciML using JuliaSim

Auto Crash Simulation

Circuitscape

JuliaSim = Science + ML

LEARN MORE ABOUT JULIASIM AND HOW IT IS BEING USED

Modeling and Simulation with JuliaSim

Dr. Chris Rackauckas

Modeling Battery Lifespan with JuliaSim

Dr. Marc Berliner

SciML: Scientific Computing + Machine Learning = Industrial Modeling for Engineers

Dr. Chris Rackauckas

Ready to see how JuliaSim can accelerate your product development? Speak to a member of our team.

Proprietary solvers and methods combining science, physics, and data.
Design Multiphysics Models - Faster, better, with reproducibility and traceability.