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ChrisRackauckas

3,832 karma · joined October 6, 2016

I am the developer of the SciML Scientific Machine Learning Open Source Software Organization (https://sciml.ai/), an organization for Julia, Python and R packages for integrated, composible, and fast scientific machine learning. I also have used quite a bit of MATLAB, C, and Fortran (well, at least used for numerical work), but these days. I think the purpose of discussing technical topics is the dissemination of ideas, so let's bring each other up not down!
submissionscomments

SciML: An Open Source Software Organization for Scientific Machine Learning

sciml.ai·15 pts·ChrisRackauckas·
0

Neural Network aided quarantine control model estimation of Covid spread (Wuhan)

arxiv.org·1 pts·ChrisRackauckas·
0

Show HN: Universal Differential Equations for Scientific Machine Learning

youtube.com·2 pts·ChrisRackauckas·
0

On the performance and design of BioSequences compared to the Seq language

biojulia.net·9 pts·ChrisRackauckas·
0

Train Interpretable Neural Networks That Accurately Extrapolate from Small Data

stochasticlifestyle.com·4 pts·ChrisRackauckas·
1

Universal Differential Equations for Scientific Machine Learning

arxiv.org·6 pts·ChrisRackauckas·
1

How to Train Interpretable ML That Accurately Extrapolates from Small Data

stochasticlifestyle.com·14 pts·ChrisRackauckas·
0

Inexact Models Can Guide Decision Making in Quantitative Systems Pharmacology

stochasticlifestyle.com·29 pts·ChrisRackauckas·
0

“#Python f-strings are amazingly fast” Twitter response

twitter.com·3 pts·ChrisRackauckas·
0

Differentialequations.jl v6.9.0: Multi-GPU Implicit Ode Solves, SciPy/R Bindings

juliadiffeq.org·5 pts·ChrisRackauckas·
6

System Identification for Hybrid Systems Using Neural Networks

arxiv.org·7 pts·ChrisRackauckas·
0

Mixing Differential Equations and Neural Networks for Physics-Informed Learning

mitmath.github.io·6 pts·ChrisRackauckas·
2

Sparsity Programming: Automated Sparsity-Aware Differentiable Programming

openreview.net·3 pts·ChrisRackauckas·
1

Show HN: Developing Julia Packages [video]

youtube.com·4 pts·ChrisRackauckas·
0

Thoughts on Tiobe's Language Ranking Methodology

juliacomputing.com·9 pts·ChrisRackauckas·
0

When do micro-optimizations matter in scientific computing?

stochasticlifestyle.com·2 pts·ChrisRackauckas·
0

The Essential Tools of Scientific Machine Learning

stochasticlifestyle.com·161 pts·ChrisRackauckas·
19

Show HN: Pharmaceutical Modeling and Simulation with Pumas (Julia)

pumas.ai·3 pts·ChrisRackauckas·
0

A Differentiable Programming System to Bridge ML and Scientific Computing

arxiv.org·381 pts·ChrisRackauckas·
71

Julia SoC '19: Quantum Algorithms for Differential Equations

nextjournal.com·17 pts·ChrisRackauckas·
0

Why you shouldn't use Euler's method to solve ODEs

nextjournal.com·5 pts·ChrisRackauckas·
0

Neural Jump SDEs (Jump Diffusions) and Neural PDEs

stochasticlifestyle.com·35 pts·ChrisRackauckas·
5

ModelingToolkit.jl, an IR and Compiler for Scientific Models

juliadiffeq.org·84 pts·ChrisRackauckas·
20

DiffEqFlux.jl – A Julia Library for Neural Differential Equations

julialang.org·207 pts·ChrisRackauckas·
14

Zero-Cost Abstractions in Julia: Indexing Vectors by Name with LabelledArrays

stochasticlifestyle.com·4 pts·ChrisRackauckas·
0

William Kahan (Father of Floating Point): A Numerical Analyst on Deep Learning

youtube.com·13 pts·ChrisRackauckas·
0

Don't Unroll Adjoint: Differentiating SSA-Form Programs

arxiv.org·1 pts·ChrisRackauckas·
0

Creating a Deep Neural Network Model to Learn Handwritten Digits Using Julia

linkedin.com·3 pts·ChrisRackauckas·
0

Some State of the Art Packages in Julia v1.0

stochasticlifestyle.com·16 pts·ChrisRackauckas·
2

Optimization Modeling Language JuMP (Julia) Joins NumFOCUS Sponsored Projects

numfocus.org·5 pts·ChrisRackauckas·
0
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