Tag Archives: scientific machine learning

Abstract: Many probabilistic programming languages (PPLs) attempt to integrate with equation solvers (differential equations, nonlinear equations, partial differential equations, etc.) from the inside, i.e. the developers of the PPLs like Stan provide differential equation solver choices as part of the suite. However, as equation solvers are an entire discipline to themselves with many active development communities and subfields, this places an immense burden on PPL developers to keep up with the changing landscape of tens of thousands of independent researchers. In this talk we will explore how Julia PPLs such as Turing.jl support of equation solvers from the outside, i.e. how the tools of differentiable programming allows equation solver libraries to be compatible with PPLs without requiring any co-development between the communities. We will discuss how this has enabled many advanced methods, such as adaptive solvers for stochastic differential equations and nonlinear tearing of differential-algebraic equations, to be integrated into the Turing.jl environment with no development effort required, and how this enables many workflows in scientific machine learning (SciML).

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Learning Epidemic Models That Extrapolate, AI4Pandemics

By: Christopher Rackauckas

Re-posted from: http://www.stochasticlifestyle.com/learning-epidemic-models-that-extrapolate-ai4pandemics/

I think this talk was pretty good so I wanted to link it here!

Title: Learning Epidemic Models That Extrapolate

Speaker Chris Rackauckas, https://chrisrackauckas.com/

Abstract:
Modern techniques of machine learning are uncanny in their ability to automatically learn predictive models directly from data. However, they do not tend to work beyond their original training dataset. Mechanistic models utilize characteristics of the problem to ensure accurate qualitative extrapolation but can lack in predictive power. How can we build techniques which integrate the best of both approaches? In this talk we will discuss the body of work around universal differential equations, a technique which mixes traditional differential equation modeling with machine learning for accurate extrapolation from small data. We will showcase how incorporating different variations of the technique, such as Bayesian symbolic regression and optimizing the choice of architectures, can lead to the recovery of predictive epidemic models in a robust way. The numerical difficulties of learning potentially stiff and chaotic models will highlight how most of the adjoint techniques used throughout machine learning are inappropriate for learning scientific models, and techniques which mitigate these numerical ills will be demonstrated. We end by showing how these improved stability techniques have been automated and optimized by the software of the SciML organization, allowing practitioners to quickly scale these techniques to real-world applications.

See more on: https://ai4pandemics.org/

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