The Julia SciML Ecosystem: Scientific Machine Learning as a Software Problem - Chris Rackauckas

The Julia SciML Ecosystem: Scientific Machine Learning as a Software Problem
Christopher V. Rackauckas, Massachusetts Institute of Technology
Abstract:
The Julia SciML ecosystem is a software suite for high performance modeling and simulation which incorporates scientific machine learning for automated model discovery and nonlinear model order reduction. This talk will introduce the audience to the philosophy of composability of the software suite by demonstrating how connections between seemingly disjoint modules can be composed to create new high performance algorithms on demand. It will start by describing the high performance simulation techniques, from new methods for numerically approximating ODEs and SDEs to all the way to methods for generating discretizations for nonlinear optimal control and automated solution of partial differential equations. From there connections to machine learning will be seen, with physics-informed neural networks (PINNs) supplementing the suite with non-local partial differential-algebraic equation solvers to universal differential equations with the ability to learn model misspecification directly from data. Connections to probabilistic programming will be discussed, mixing Bayesian estimation with scientific machine learning, along with high performance computing via CPU and GPU parallelism. This talk will thus set the stage for the following speakers who will demonstrate specific methodologies and applications within this application sphere.
For more information on the SciML Open Source Scientific Machine Learning Software Ecosystem, see sciml.ai/.
For more info on the Julia Programming Language, follow us on Twitter: / julialanguage
Contents
00:00 Overview
00:21 Objective: The aim of the SciML ecosystem
01:24 Definition: What is scientific machine learning
02:45 Example: An application to ocean columns modelling
07:25 Hard Problem: How to fit a neural network inside a simulator
10:59 No Silver Bullet: Different adjoint methods for different problems
13:24 SciML vs. The Rest: A comparison with other libraries in other languages
14:38 SciML Today: The current state of the SciML ecosystem
15:35 Conclusion: Further developments
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[@asadhussain6262]

"You can't necessarily solve the entire ocean" - Chris Rackauckas

[@SHAINON117]

Excellent ❤❤❤❤❤❤❤

[@_carlisto_]

This is awesome!

[@kamilziemian995]

Very impressive, but since this is quite dense talk I didn't understood many things.