diffeqtorch 0.1.1

Bridges `DifferentialEquations.jl` with PyTorch.

diffeqtorch

Bridges DifferentialEquations.jl with PyTorch. Besides benefitting from the huge range of solvers available in DifferentialEquations.jl, this allows taking gradients through solvers using local sensitivity analysis/auto-diff. The package has only been tested with ODE problems, and in particular, automatic differentiation is only supported for ODEs using ForwardDiff.jl. This can be extended in the future, contributions are welcome.

Examples

• Simple ODE problem to demonstrate the interface and confirm gradients with analytical solution
• SIR model for a slighlty more complicated model with numerical gradient checking
• Hodgkin-Huxley model for a realistic example from Neuroscience

Installation

Prerequisites for using diffeqtorch are installation of Julia and Python. Note that the binary directory of julia needs to be in your PATH.

We recommend using a custom Julia sytem image containing dependencies. If the environment variable JULIA_SYSIMAGE_DIFFEQTORCH is set, the installation script will automatically build the image. This may take a while but will improve speed afterwards.

Install diffeqtorch:

$ export JULIA_SYSIMAGE_DIFFEQTORCH="$HOME/.julia_sysimage_diffeqtorch.so"
$ pip install diffeqtorch -v

Usage

from diffeqtorch import DiffEq

f = """
function f(du,u,p,t)
    du[1] = p[1] * u[1]
end
"""
de = DiffEq(f)

u0 = torch.tensor([1.])
tspan = torch.tensor([0., 3.])
p = torch.tensor([1.01])

u, t = de(u0, tspan, p)

See also help(DiffEq) and examples provided in notebooks/.

License

MIT