Julia Computing Introduces JuliaSim For Cloud-Based Scientific Machine Learning
Tags: datascience, programming, machinelearning
Published: 2021-06-15 09:41:22

<p style="color: rgb(14, 16, 26); background: transparent; margin-top:0pt; margin-bottom:0pt;"><span data-preserver-spaces="true" style="color: rgb(14, 16, 26); background: transparent; margin-top:0pt; margin-bottom:0pt;">JuliaSim is a next-generation cloud-based modeling and simulation platform that combines cutting-edge machine learning techniques with equation-based modeling and simulation.</span></p>
<p style="color: rgb(14, 16, 26); background: transparent; margin-top:0pt; margin-bottom:0pt;"><span data-preserver-spaces="true" style="color: rgb(14, 16, 26); background: transparent; margin-top:0pt; margin-bottom:0pt;">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.</span></p>
<p style="color: rgb(14, 16, 26); background: transparent; margin-top:0pt; margin-bottom:0pt;"><span data-preserver-spaces="true" style="color: rgb(14, 16, 26); background: transparent; margin-top:0pt; margin-bottom:0pt;">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.</span></p>
<p style="color: rgb(14, 16, 26); background: transparent; margin-top:0pt; margin-bottom:0pt;"><span data-preserver-spaces="true" style="color: rgb(14, 16, 26); background: transparent; margin-top:0pt; margin-bottom:0pt;">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&apos;s workflow, saving time on model development and simulation.</span></p>
<p style="color: rgb(14, 16, 26); background: transparent; margin-top:0pt; margin-bottom:0pt;"><span data-preserver-spaces="true" style="color: rgb(14, 16, 26); background: transparent; margin-top:0pt; margin-bottom:0pt;">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.</span></p>
<p style="color: rgb(14, 16, 26); background: transparent; margin-top:0pt; margin-bottom:0pt;"><span data-preserver-spaces="true" style="color: rgb(14, 16, 26); background: transparent; margin-top:0pt; margin-bottom:0pt;">Several of JuliaSim&apos;s advantages include the following:</span></p>
<ul>
    <li style="color: rgb(14, 16, 26); background: transparent; margin-top:0pt; margin-bottom:0pt;"><strong style="color: rgb(14, 16, 26); background: transparent; margin-top:0pt; margin-bottom:0pt;"><span data-preserver-spaces="true" style="color: rgb(14, 16, 26); background: transparent; margin-top:0pt; margin-bottom:0pt;">Accelerate with Surrogates:</span></strong><span data-preserver-spaces="true" style="color: rgb(14, 16, 26); background: transparent; margin-top:0pt; margin-bottom:0pt;">&nbsp;JuliaSim enables users to generate fast approximate models using cutting-edge scientific machine learning and model order reduction techniques.</span></li>
    <li style="color: rgb(14, 16, 26); background: transparent; margin-top:0pt; margin-bottom:0pt;"><strong style="color: rgb(14, 16, 26); background: transparent; margin-top:0pt; margin-bottom:0pt;"><span data-preserver-spaces="true" style="color: rgb(14, 16, 26); background: transparent; margin-top:0pt; margin-bottom:0pt;">Integrate with Uncertainty Quantification and Noise:</span></strong><span data-preserver-spaces="true" style="color: rgb(14, 16, 26); background: transparent; margin-top:0pt; margin-bottom:0pt;">&nbsp;Users can create designs resistant to uncertainty and stochasticity by utilizing advanced techniques such as Polynomial Chaos and Koopman Operator approaches.</span></li>
    <li style="color: rgb(14, 16, 26); background: transparent; margin-top:0pt; margin-bottom:0pt;"><span data-preserver-spaces="true" style="color: rgb(14, 16, 26); background: transparent; margin-top:0pt; margin-bottom:0pt;">Integrate Julia&apos;s differentiable programming for high-performance, stable adjoints to accelerate parameter estimation and optimization.</span></li>
    <li style="color: rgb(14, 16, 26); background: transparent; margin-top:0pt; margin-bottom:0pt;"><span data-preserver-spaces="true" style="color: rgb(14, 16, 26); background: transparent; margin-top:0pt; margin-bottom:0pt;">Combining models with tools such as DiffEqFlux and NeuralPDE enables discovering missing physics and the generation of digital twins.</span></li>
    <li style="color: rgb(14, 16, 26); background: transparent; margin-top:0pt; margin-bottom:0pt;"><span data-preserver-spaces="true" style="color: rgb(14, 16, 26); background: transparent; margin-top:0pt; margin-bottom:0pt;">Use advanced numerical tools such as discontinuity-aware differential equation solvers, high-performance steady-state solvers, and domain-specific environments.</span></li>
    <li style="color: rgb(14, 16, 26); background: transparent; margin-top:0pt; margin-bottom:0pt;"><strong style="color: rgb(14, 16, 26); background: transparent; margin-top:0pt; margin-bottom:0pt;"><span data-preserver-spaces="true" style="color: rgb(14, 16, 26); background: transparent; margin-top:0pt; margin-bottom:0pt;">Utilize Pre-Built Models and Digital Twins:</span></strong><span data-preserver-spaces="true" style="color: rgb(14, 16, 26); background: transparent; margin-top:0pt; margin-bottom:0pt;">&nbsp;Users can access complete models from the JuliaSim Model Store and assemble the pieces to expedite the design process.</span></li>
</ul>