Julia Computing Introduces JuliaSim For Cloud-Based Scientific Machine Learning

JuliaSim is a next-generation cloud-based modeling and simulation platform that combines cutting-edge machine learning techniques with equation-based modeling and simulation.

Julia Computing recently announced the release of JuliaSim, a cloud-based simulation reality platform for scientific machine learning. JuliaSim is a next-generation cloud-based modeling and simulation platform, according to sources.

It combines cutting-edge scientific machine learning techniques with equation-based digital twin modeling and simulation. Modern machine learning-based techniques accelerate simulation up to 500 times, fundamentally altering what is possible with computational design.

JuliaSim enables users to import models directly from its Model Store into their Julia environment, simplifying creating extensive complex simulations. Pre-trained machine learning models based on SciML are seamlessly integrated into the engineer's workflow, saving time on model development and simulation.

JuliaSim enables users to design physical products and reduce iterations by creating high-fidelity designs, automatically converting them to accelerated versions, and searching vast parameter spaces.

Several of JuliaSim's advantages include the following:

• Accelerate with Surrogates: JuliaSim enables users to generate fast approximate models using cutting-edge scientific machine learning and model order reduction techniques.
• Integrate with Uncertainty Quantification and Noise: Users can create designs resistant to uncertainty and stochasticity by utilizing advanced techniques such as Polynomial Chaos and Koopman Operator approaches.
• Integrate Julia's differentiable programming for high-performance, stable adjoints to accelerate parameter estimation and optimization.
• Combining models with tools such as DiffEqFlux and NeuralPDE enables discovering missing physics and the generation of digital twins.
• Use advanced numerical tools such as discontinuity-aware differential equation solvers, high-performance steady-state solvers, and domain-specific environments.
• Utilize Pre-Built Models and Digital Twins: Users can access complete models from the JuliaSim Model Store and assemble the pieces to expedite the design process.