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🦏
Wandering
Ilya Orson
IlyaOrson
🦏
Wandering
RL for cyberdefence @ The Alan Turing Institute 💻 PhD candidate @ ImperialCollege 📚 RL and optimal control 🚀 Previously - Data Science & Physics
Symbolic Regression with Conformal Prediction Intervals.
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The Jax developers optimized a differential equation benchmark in this issue
which used DiffEqFlux.jl as a performance baseline. The Julia code from there was updated to include some standard performance
tricks and is the benchmark code here. Thus both codes have been optimized by the library developers.
Klein Bottle Math Politcal Compass meme animation (Julia)
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torchdiffeq vs Julia DiffEqflux Neural ODE Training Benchmark
torchdiffeq vs Julia DiffEqFlux Neural ODE Training Benchmark
The spiral neural ODE was used as the training benchmark for both torchdiffeq (Python)
and DiffEqFlux (Julia) which utilized the same architecture and 500 steps of ADAM. Both achived
similar objective values at the end. Results:
torchdiffeq (Python) vs DifferentialEquations.jl (Julia) ODE Benchmarks (Neural ODE Solvers)
Torchdiffeq vs DifferentialEquations.jl (/ DiffEqFlux.jl) Neural ODE Compatible Solver Benchmarks
Only non-stiff ODE solvers are tested since torchdiffeq does not have methods for stiff ODEs. The ODEs
are chosen to be representative of models seen in physics and model-informed drug development (MIDD)
studies (quantiative systems pharmacology) in order to capture the performance on realistic scenarios.
Summary
Below are the timings relative to the fastest method (lower is better). For approximately 1 million
ODEs and less, torchdiffeq was more than an order of magnitude slower than DifferentialEquations.jl