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

Understand how robust MPC takes parameter uncertainty into account ensuring that the controller achieves good performance and constraint satisfaction despite uncertainty in the model

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Julia collaboration has taken a revolutionary leap forward with JuliaHub Projects.

Wednesday, June 14th | 10:30 AM ET (US)

Calibrating Models to Difficult and Noisy Data with JuliaSim Model Optimizer


The outputs of a model cannot be interpreted as true probabilities without calibration. This requires you to de-noise the data and set up an optimization routine. Learn how you can focus on modeling and interpretation while leaving the details of model calibration to an automated system. In this webinar, Dr. Ranjan Anantharam will share how to leverage the model calibration and parameter estimation algorithms in JuliaSim Model Optimizer to perform noise-robust model calibration.

Attendees will learn: -

  • Inverse problems and how to specify them 
  • Model calibration algorithms and when to choose them 
  • How to import/specify models into JuliaSim

Model, calibrate, and optimize with ease - Register to find out how!

Can't join us for the live event? Webinar registrants receive a recording of the webinar after the event.

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Meet Your Speaker


Dr. Ranjan Anantharam

Sales Engineering @ JuliaHub

Dr. Ranjan Anantharam has a PhD in Mathematics & Computational Science from the Massachusetts Institute of Technology. He is a sales engineer at JuliaHub, where he helps customers leverage modern computational methods through JuliaSim.


JuliaSim Control 

  • Model-predictive control (MPC) for linear and nonlinear systems

  • Robust MPC for uncertain systems

  • GUI apps for autotuning and model reduction

  • PID autotuning to automate workflows and quickly tune PID controllers

Model Discovery

Combine models with tools like DiffEqFlux and NeuralPDE to discover missing physics and generate digital twins.

Combine with Pre-Built Models and Digital Twins

Grab complete models from the JuliaSim Model Store and compose the pieces to accelerate the design process.

Specialized Numerical Environments

Use the latest numerical tools, like discontinuity-aware differential equation solvers, high-performance steady state solvers, and domain-specific environments.

Blending classical physical modeling with modern Scientific Machine Learning techniques.


JuliaSim is a next generation cloud-based simulation platform, combining the latest techniques in Scientific Machine Learning with equation-based digital twin modeling and simulation. Our modern ML-based techniques accelerate simulation by up to 500x, changing the paradigm of what is possible with computational design. The premise of the software is to facilitate the design and accelerate challenging real-life models of considerable complexity.


JuliaSim allows the user to import models directly from the Model Store (more information below) into the Julia environment, making it easy to build large complex simulations. The user-friendly GUI facilitates the process and makes simulation more accessible to a wider audience.


JuliaSim produces surrogates of blackbox (and regular) dynamical systems using Continuous Time Echo State Networks (CTESNs). This novel technique allows, amongst other features, for implicit training in parameter space to stabilize the ill-conditioning present in stiff systems.

Learn more about the JuliaSim Ecosystem

Julia Computing delivers JuliaSim as an answer to accelerating simulations through digital-twin (or surrogate) modeling. By blending classical, physical modeling with advanced scientific machine learning (SciML) techniques, JuliaSim provides a next-generation platform for building, accelerating, and analyzing models.