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Faster Surrogate Model Simulation - Accelerating Inverse PDE Problems

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On-Demand Webinar

Engineers working on complex PDE systems grapple with computationally expensive inverse problems require substantial time and resources for parameter estimation and sensitivity analysis. Time-to-design can be substantially improved by leveraging JuliaSim and DigitalEcho to generate surrogate models of PDE systems.

In this webinar, Dr. Ranjan Anantharaman explains how to build surrogate models of PDE Systems and use them in downstream inverse problems. We will walk you through the steps to use JuliaSim surrogates and DigitalEcho technology, resulting in 100x faster time-to-optimal design and sensitivity analysis.

Learn how to:

  • Generate a surrogate model using JuliaSim Surrogates using large batch compute on the JuliaHub platform

  • Define an inverse problem using JuliaSim ModelOptimizer

  • Use a surrogate model to accelerate the optimization of the inverse problem

Speakers

Ranjan
Ranjan Anantharaman
Sales Engineer
Ranjan is a sales engineer at JuliaHub, where he helps customers leverage modern engineering workflows and scientific machine learning using JuliaSim. He has a PhD in Mathematics & Computational Science from MIT, where his thesis work centered on surrogate modeling of dynamical systems.

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