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SUMMARY:Fast Higher-order Automatic Differentiation for Physical Models - 
 Songchen Tan
DTSTART;TZID=US/Eastern:20230726T103000
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DTSTAMP:20260817T235725Z
UID:pretalx-juliacon2023-D8RHE7@pretalx.com
DESCRIPTION:Taking higher-order derivatives is crucial for physical models
  like ODEs and PDEs\, and it would be great to get it done by automatic di
 fferentiation. Yet\, existing packages in Julia either has exponential sca
 ling w.r.t. order (nesting first-order AD) or has exponential scaling w.r.
 t. dimension (nested Taylor polynomial in TaylorSeries.jl). The author pre
 sents TaylorDiff.jl (https://github.com/JuliaDiff/TaylorDiff.jl) which is 
 specifically optimized for fast higher-order directional derivatives.
LOCATION:Online talks and posters
URL:https://pretalx.com/juliacon2023/talk/D8RHE7/
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