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Wednesday, April 17th | 1:00 PM ET (US)
Comparative Analysis of Cell Chemistries with JuliaSim Batteries
Navigating the complexities of numerous battery chemistries and form factors can be challenging when seeking the perfect fit for your application. Numerous battery chemistries and form factors are manufactured and sold but it is not always clear what battery is best for your specific application. In this webinar, we'll demystify the process of choosing the right battery by introducing you to the JuliaSim Batteries GUI. Join Dr. Marc Berliner as he showcases JuliaSim Batteries GUI to compare and contrast various battery chemistries and physics-based battery models for specific use cases such as fast charging. Learn how to predict crucial metrics such as the cycle number at end-of-life, making informed decisions for your unique application.
In this webinar, explore:
- Utilizing JuliaSim Batteries GUI: Set up multilple battery chemistries with our intuitive GUI
- Simple and Complex Charge/Discharge Procedures: Learn to configure and execute both simple and complex discharge processes
- Data Comparison and Analysis: Download simulation data to compare and contrast results across simulations or against experimental data.
- Live Demo: Witness the power of JuliaSim Batteries GUI in real-time scenarios
Q&A: Our experts will be on hand to answer your questions and provide insights into making informed battery choices.
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,.
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.