Free Webinars from Julia Computing

1. Trimming, Linearization, and Model Predictive Control (MPC) Design with Dyad (Formerly JuliaSim): Click here to register for a free Dyad (Formerly JuliaSim) Webinar, Trimming, Linearization, and Model Predictive Control (MPC) Design with Dyad (Formerly JuliaSim) on Wednesday November 2 from 10-11 am Eastern (US). The Webinar is led by Dr. Fredrik Bagge Carlson (Julia Computing Control Systems Team Lead) and Dr. Avinash Subramanian (Dyad Control Systems). Dyad (Formerly JuliaSim) is a next-generation simulation platform from Julia Computing that combines the latest Scientific Machine Learning (SciML) techniques with equation-based digital twin modeling and simulation. This introductory Webinar is open to anyone familiar with the language of model predictive control (MPC) and related fields. In this Webinar, Dr. Carlson and Dr. Subramanian will demonstrate the typical workflow for a control engineer with Dyad (Formerly JuliaSim). They will show a demo using a realistic case study of controlling a non-linear Research Civil Aircraft Model (RCAM) and present the various steps starting from building the dynamic system model using ModelingToolkit, trimming, linearization about the trim point, as well as the formulation and solution of a closed-loop linear MPC model together with the non-linear observer and plant model. Lastly, a new MPC surrogatization feature will be presented that allows for fast, optimizer-free closed-loop control by learning the control law from data. Space is limited, so please register now to reserve your spot.

2. Dyad (Formerly JuliaSim) ModelOptimizer: Model Calibration and Parameter Estimation: Click here to register for a free Dyad (Formerly JuliaSim) Webinar, Dyad (Formerly JuliaSim) ModelOptimizer: Model Calibration and Parameter Estimation on Wednesday November 9 from 12-1 pm Eastern (US). The Webinar is led by Jacob Vaverka (Julia Computing Sales Engineer). Dyad (Formerly JuliaSim) is a next-generation simulation platform from Julia Computing that combines the latest Scientific Machine Learning (SciML) techniques with equation-based digital twin modeling and simulation. This introductory Webinar is open to anyone familiar with the language of model predictive control (MPC) and related fields. In this Webinar, participants will learn how to set up a modeling problem, how to create the inverse problem for parameter estimation and how to generate and visualize the optimized parameters. Space is limited, so please register now to reserve your spot.

3. Fast and Furious Development with JuliaHub Workflows: Click here to register for a free Julia Computing Webinar, Fast and Furious Development with JuliaHub Workflows, on Thursday November 10 from 1-2 pm Eastern (US). The Webinar is led by Deep Datta (Julia Computing Director of Product Management). JuliaHub is the fastest, easiest way to access Julia, the fastest and most powerful high performance open source programming language for scientific and numerical computing. Webinar participants will learn how JuliaHub tooling provides the easiest on-ramp for enterprise development, how to use the Julia package registry, bring new Julia packages into JuliaHub, create a private package repository and bring continuous integration into your workflows. Space is limited, so please register now to reserve your spot.

4. Hierarchical Bayesian Methods for Efficient Generation of Virtual Populations for Quantitative Systems Pharmacology (QSP): Click here to register for a free PumasQSP Webinar, Hierarchical Bayesian Methods for Efficient Generation of Virtual Populations for Quantitative Systems Pharmacology (QSP) on Wednesday November 16 from 10-11 am Eastern. The Webinar is led by Dr. Haris Organtzidis (Julia Computing Research Engineer). PumasQSP is a Julia package and methodology for quantitative systems pharmacology to solve difficult model calibration and analyses in a high-performance and user-friendly manner. In this Webinar, participants will learn how to improve construction of large, expensive virtual population sets, the advantages of advanced virtual population generation methods compared with more standard naive methods, why naïve virtual population methods are inefficient and have limited features, how hierarchical Bayesian methods can be used to generate virtual populations more efficiently by explicitly modeling a population of patients, noise in the data, and domain expertise in the form of prior distributions and how to use these Bayesian methods with Julia Computing’s PumasQSP. Space is limited, so please register now to reserve your spot.

Julia Computing - Coming to a Conference Near You:

• Dallas, TX: American Modelica Conference with Julia Computing Oct 26-28

• Aurora, CO: American Conference on Pharmacometrics (ACoP) with Julia Computing and Pumas-AI Oct 30-Nov 2

• Orlando, FL: Interservice/Industry Training, Simulation and Education Conference (I/ITSEC) with Julia Computing Nov 28-Dec 2

• New Orleans, LA: Conference on Neural Information Processing Systems (NeurIPS) with Julia Computing Nov 28-Dec 9

Julia Computing CEO Dr. Viral Shah at Intel Innovation:

Dr. Viral Shah (Julia Computing CEO, co-founder of Julia Computing, co-creator of Julia) joined a panel at Intel to discuss Accelerating Developer Innovation through an Open Ecosystem. Click here to watch.

Julia at ASML Masterclass:

ASML, ‘The World’s Supplier to the Semiconductor Industry’ hosted a Julia at ASML masterclass. Contact info@juliacomputing.com if you would like to publicize or organize a corporate Julia event for your organization.

Automatic Differentiation of Programs with Discrete Randomness and Julia for Climate Change:

Automatic Differentiation of Programs with Discrete Randomness and Capturing Missing Physics in Climate Model Parameterizations Using Neural Differential Equations are two new papers co-authored by Dr. Chris Rackauckas, Julia Computing Director of Modeling and Simulation. Click here to read Automatic Differentiation of Programs with Discrete Randomness and Click here to read Capturing Missing Physics in Climate Model Parameterizations Using Neural Differential Equations.

NASA Sea Level Change Portal Powered by Julia:

The NASA Sea Level Change Portal uses OceanStateEstimation.jl to provide an ‘open-source cloud-native platform with simple Webservice APIs to access the portal’s data, analysis and visualizations’. Click here for more information.

Free Intro to Julia Short Course from Texas A&M High Performance Research Computing (HPRC):

Texas A&M has produced a free Intro to Julia short course presented by Professor Jian Tao. The course is 2 hours and 46 minutes and includes basic language elements and concepts, programming best practice and relevant open source tools.

Julia for Beer:

Javier (@runjaj) uses ModelingToolkit.jl with Pluto.jl to model beer fermentation. The work is based on Optimal Temperature Control for Batch Beer Fermentation (Gee & Ramirez 1988).

Converting from Proprietary Software to Julia:

Are you looking to leverage Julia’s superior speed and ease of use, but limited due to legacy software and code? Julia Computing and our partners can help accelerate replacing your existing proprietary applications, improve performance, reduce development time, augment or replace existing systems and provide an extended trusted team to deliver Julia solutions. Leverage experienced resources from Julia Computing and our partners to get your team up and running quickly. For more information, please contact us.

Careers at Julia Computing:

Julia Computing is a fast-growing tech company with fully remote employees in 12 countries on 5 continents. Click here to learn more about exciting careers and internships with Julia Computing.

Julia and Julia Computing in the News

• Bits & Chips: Ground-Breaking Innovations in Ground-Penetrating Radar

• Tech Beacon: 8 Languages for Data Science

Julia for Epidemiology:

Raj Dandekar has published two new blog posts: Julia for Epidemiology and Scientific Machine Learning for Epidemiologists - Part 1. The blog posts are available here and the code is available here.

Julia Blog Posts

• 5 Free Courses to Learn Julia - Start Learning Today! (Logan Kilpatrick)

• My Favorite Programming Languages I Used ​​for Scientific Computing (Josef Cruz)

• Knight Covering Puzzle (Bogumił Kamiński)

• 130 Graded Exercises to Train Your Julia for Data Analysis Muscle (Bogumił Kamiński)

• Is DataFrames.jl Hamiltonian? (Bogumił Kamiński)

• Weird Syntax!: Julia Constants (Emmett Boudreau)

• Chifi Source Community Update (Emmett Boudreau)

• The Olive Project Update (Emmett Boudreau)

• Weird Syntax!: Parametric Symbolism (Emmett Boudreau)

• Constructors In Review: Field Types And Parameters (Emmett Boudreau)

• Is It A Method Or A Function? (Emmett Boudreau)

• SCIP Plugins and the Cut Selection Interface (Mathieu Besançon)

• Pruning the Expression Tree with Recursive Value Identification (Mathieu Besançon)

• Julia on Kaggle (Justin Ochalek)

• Tables Output with Literate.jl (Tamás K. Papp)

• Extracting Data from Harry Potter with GPT-3 (Julius Krumbiegel)

• Direct Automatic Differentiation of (Differential Equation) Solvers vs Analytical Adjoints: Which is Better? (Chris Rackauckas)

Upcoming Julia and Julia Computing Events

• Dallas, TX: American Modelica Conference with Julia Computing Oct 26-28

• Aurora, CO: American Conference on Pharmacometrics (ACoP) with Julia Computing and Pumas-AI Oct 30-Nov 2

• Webinar: Trimming, Linearization, and Model Predictive Control (MPC) Design with Dyad (Formerly JuliaSim) with Dr. Fredrik Bagge Carlson (Julia Computing) and Dr. Avinash Subramanian (Dyad Control Systems) Nov 2

• Webinar: Dyad ModelOptimizer: Model Calibration and Parameter Estimation with Jacob Vaverka (Julia Computing) Nov 9

• Webinar: Fast and Furious Development with JuliaHub Workflows with Deep Datta (Julia Computing) Nov 10

• Webinar: Hierarchical Bayesian Methods for Efficient Generation of Virtual Populations for Quantitative Systems Pharmacology (QSP) with Dr. Haris Organtzidis (Julia Computing) Nov 16

• Orlando, FL: Interservice/Industry Training, Simulation and Education Conference (I/ITSEC) with Julia Computing Nov 28-Dec 2

• New Orleans, LA: Conference on Neural Information Processing Systems (NeurIPS) with Julia Computing Nov 28-Dec 9

Recent Julia and Julia Computing Events

• San Jose, CA: Intel Innovation with Dr. Viral Shah (Julia Computing) Sept 27-28

• Online: Julia – The New Kid on the Block for Machine Learning, Data Science and More with ObjektForum Karlsruhe Oct 17

• Webinar: Building Digital Twins with JuliaSim with Dr. Raj Abhijit Dandekar (Julia Computing) Oct 18

• Masterclass: Julia at ASML Oct 18

Contact Us:

Please contact us if you wish to:

• Purchase or obtain license information for products such as JuliaHub, JuliaSim or Pumas

• Obtain pricing for Julia consulting projects for your organization

• Schedule online Julia training for your organization

• Share information about exciting new Julia case studies or use cases

• Spread the word about an upcoming online event involving Julia

• Partner with Julia Computing to organize a Julia event online

• Submit a Julia internship, fellowship or job posting

About Julia Computing and Julia

Julia Computing's mission is to develop products that bring Julia's superpowers to its customers. JuliaHub is a secure, software-as-a-service platform for developing Julia programs, deploying them, and scaling to thousands of nodes. It provides the power of a supercomputer at the fingertips of every data scientist and engineer. In addition to data science workflows, Julia Computing also provides access to cutting-edge products such as Pumas for pharmaceutical modeling and simulation, JuliaSim for multi-physics modeling and simulation, and Cedar for electronic circuit simulation, combining traditional simulation with modern SciML approaches.

Julia is the fastest high performance open source computing language for data, analytics, algorithmic trading, machine learning, artificial intelligence, and other scientific and numeric computing applications. Julia solves the two language problem by combining the ease of use of Python and R with the speed of C++. Julia provides parallel computing capabilities out of the box and unlimited scalability with minimal effort. Julia has been downloaded by users at more than 10,000 companies and is used at more than 1,500 universities. Julia co-creators are the winners of the 2019 James H. Wilkinson Prize for Numerical Software and the 2019 Sidney Fernbach Award. Julia has run at petascale on 650,000 cores with 1.3 million threads to analyze over 56 terabytes of data using Cori, one of the ten largest and most powerful supercomputers in the world.