Skip to content

The modern platform for technical computing

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

Thursday, Feb 22th | 1:00 PM ET (US)

Next-Gen Battery Simulation: Solving 1,000 Cell Electrochemical Battery Packs with JuliaSim

Struggling with slow simulation cycles and limited models when simulating large battery packs? Join Dr. Marc D. Berliner, lead developer of JuliaSim Batteries and Dr. Chris Rackauckas, VP of Modeling and Simulation in this Webinar to solve one of the most pressing challenges in the battery industry - inefficiency in simulating large and complex battery packs using conventional tools. Existing methods and traditional tools sacrifice accuracy for speed to achieve real-time performance at the pack level. JuliaSim Batteries enables faster-than-real-time performance of complex partial differential equations (PDEs) of large battery packs while addressing defects arising from battery manufacturing.

Who must attend: Professionals in the battery industry, including battery manufacturers, researchers, engineers in electric vehicles, renewable energy, consumer electronics, and anyone working on battery pack simulation.

Key takeaways:

  • Efficient simulation techniques for complex battery simulations
  • Explore parametric distributions to understand how manufacturing defects influence overall state of health (SOH) 
  • Demo of simulating large battery packs with 1,000 cells in real time using electrochemical battery models in JuliaSim Batteries 
  • Witness the transformative capabilities of JuliaSim Batteries to empower professionals across diverse industries.

Elevate your battery simulation capabilities, make informed decisions, and accelerate innovation in the dynamic space of battery technology.

Register now!



Meet Your Speakers

Marc Berliner

Dr. Marc D. Berliner

Lead Developer JuliaSim Batteries
Dr. Marc D. Berliner is the lead developer of JuliaSim Batteries at JuliaHub, Inc. He received a Ph.D. from the MIT Department of Chemical Engineering, where his work focused on high-performance simulation of physics-based lithium-ion battery models, parameter estimation techniques, and optimal charging algorithms,.
Christopher Rackauckas - OLD

Dr. Christopher Rackauckas

VP of Modeling and Simulation
Dr. Chris Rackauckas is the VP of Modeling and Simulation at JuliaHub, the Director of Scientific Research at Pumas-AI,  Co-PI of the Julia Lab at MIT, and the lead developer of the SciML Open Source Software Organization.

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.