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Tuesday, March 19th | 1:00 PM ET (US)
Revolutionizing Battery Quality: Strategies for Battery Defect Mitigation using JuliaSim
Manufacturing defects introduce considerable variation among battery cells, challenging the uniformity of production batches. OEMs often struggle to deal with the impact of defects on performance and lifetime prediction. Join Dr. Marc Berliner and Dr. Ranjan Anantharaman to delve deeper into the production process, decoding how manufacturing defects influence key metrics such as performance, and state of health (SOH) by leveraging JuliaSim Batteries GUI.
Practical Insights for Battery Manufacturers and Battery Engineers:
- Use the JuliaSim Batteries GUI to set up and simulate battery experiments
- Identify the impact of various defects on overall cell performance, identifying defects which truly matter
- Learn how to integrate JuliaSim Batteries GUI into your production line, enabling the classification of batches into "high-performance" and "low-performance" cells
The webinar will include a live demo and Q&A session with our team.
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,.
Dr. Ranjan Anantharaman
Dr. Ranjan Anantharaman is a Sales Engineer at JuliaHub Inc, specializing in JuliaSim's modeling and simulation offerings. He has a PhD in Mathematics & Computational Science from MIT and his expertise lies in shaping the future of simulation workflows with scientific machine learning. Ranjan collaborates with engineering firms, leveraging his background in surrogate modeling for high-dimensional dynamical systems developed at JuliaLab (MIT CSAIL). He has actively contributed to the Julia community, chairing JuliaCon since 2020.
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.