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Hi all. Is there a way to deal with this problem in Julia?
tspan = (0.0,π)
y0 = 0.0
problem = ODEProblem(explicit,y0,tspan)
sol = solve(problem, Rosenbrock23())
In MATLAB, Cleve Moler solve this problem by add a bound like this:
f = @(t,y) sqrt(1-min(y,1)^2).
This could also be used in Python with Scipy.odeint.
def f(t,y): return np.sqrt(1-np.min(y,1)**2)
But Julia don't allow this.
Hi everyone, first off, thanks for developing an awesome DE library/framework. It's really impressive just how much functionality you all have crammed into this library. I hope I'm in the right place to ask this.
I'm attempting to install the
diffeqpy package to utilize the
DifferentialEquations.jl library as a sort of "drop-in" solver to our already existing systems biology models written in Python since we need a speedup and easier parallelization options.
I'm on an Ubuntu 18.04 system and I've
PyCall.jland built it with the Python binary I'm using with my
diffeqpypackage to the virtual environment via
pipenv install(backended with pip, just in the virtual environment)
python-jlwith only benchmarking the solving of the problem, not the specification via
python-jlwith only benchmarking the solving of the problem as in (d.) and also pre-compiling the problem via
These are the results I've obtained:
You can see the full code at https://github.com/dcolli23/benchmarking_diffeqpy
I'm almost certain I've done something wrong here since this performance is vastly different and I know DifferentialEquations.jl to be some of the fastest implementations of ODE solvers out there. Do you all have any idea what I could have done wrong in my setup of the tech stack or if I'm just interfacing with the solver incorrectly?
I seriously appreciate your all's help in all of this! Thank you so much! And I'm happy to provide any extra information as needed!
Hello, I am trying to fit an ODE with 8 external inputs from measurement data which are interpolated and I find BFGS very slow compared to BlackBoxOptim. I also tried ADAM from DiffEqFlux and it is also very slow. Does anyone here have any thoughts on why ?
I am using
LinearInterpolation() objects to get the inputs at each time
Interpolations.jl, the rest are constants enclosed in a wrapper function.
function thermal_model!(du, u, p, t) # scaling parameters p_friction, p_forcedconv, p_tread2road, p_deflection, p_natconv, p_carcass2air, p_tread2carcass, p_air2ambient = p fxtire = fxtire_itp(t) fytire = fytire_itp(t) fztire = fztire_itp(t) vx = vx_itp(t) alpha = alpha_itp(t) kappa = kappa_itp(t) r_loaded = r_loaded_itp(t) h_splitter = h_splitter_itp(t) # arc length of tread area theta_1 = acos(min(r_loaded - h_splitter, r_unloaded) / r_unloaded) theta_2 = acos(min(r_loaded, r_unloaded) / r_unloaded) area_tread_forced_air = r_unloaded * (theta_1 - theta_2) * tire_width area_tread_contact = tire_width * 2 * sqrt(max(r_unloaded^2 - r_loaded^2, 0)) q_friction = p_friction * vx * (abs(fytire * tan(alpha)) + abs(fxtire * kappa)) q_tread2ambient_forcedconv = p_forcedconv * h_forcedconv * area_tread_forced_air * (t_tread - t_ambient) * vx^0.805 q_tread2ambient_natconv = p_natconv * h_natconv * (area_tread - area_tread_contact) * (t_tread - t_ambient) q_tread2carcass = p_tread2carcass * h_tread2carcass * area_tread * (t_tread - t_carcass) q_carcass2air = p_carcass2air * h_carcass2air * area_tread * (t_carcass - t_air) q_carcass2ambient_natconv = p_natconv * h_natconv * area_sidewall * (t_carcass - t_ambient) q_tread2road = p_tread2road * h_tread2road * area_tread_contact * (t_tread - t_track) q_deflection = p_deflection * h_deflection * vx * abs(fztire) q_air2ambient = p_air2ambient * h_natconv * area_rim * (t_air - t_ambient) du = der_t_tread = (q_friction - q_tread2carcass - q_tread2road - q_tread2ambient_forcedconv - q_tread2ambient_natconv)/(m_tread * cp_tread) du = der_t_carcass = (q_tread2carcass + q_deflection - q_carcass2air - q_carcass2ambient_natconv)/(m_carcass * cp_carcass) du = der_t_air = (q_carcass2air - q_air2ambient)/(m_air * cp_air) end
would be fine for your problem (just like the other cases)
function explicit(y,p,t) sqrt(1-y^2) end
[slack] <torkel.loman> Is there something I can do to help solve https://discourse.julialang.org/t/strange-maxiters-numeric-instability-occured-when-solving-certain-sde/49392/9 ?
Happy to do some digging, but unsure what I should be looking for.
((((((1.0 * α₁ * (0.0 + (10000.0 * (1.0 - (1.0 / (1.0 + exp(-20.0 * (t - 24.0)))))))) / (α₂ + (0.0 + (10000.0 * (1.0 - (1.0 / (1.0 + exp(-20.0 * (t - 24.0))))))))) - (((1.0 * α₄) * x₁(t)) / (α₅ + x₁(t)))) - ((x₁(t) * α₆₂) / 0.1048)) + (((α₆₅ * x₃(t)) / ((α₆₆ + x₃(t)) * ((0.1048 * ((x₁(t) + x₇(t)) + ((((x₉(t) + x₁₀(t)) + x₁₁(t)) + x₁₂(t)) * 2))) / α₇₁))) / 0.1048)) + ((((x₉(t) + x₁₀(t)) + x₁₁(t)) + x₁₂(t)) * α₂₃)) - ((x₁(t) * x₅(t)) * α₂₄)
ERROR: Failed to apply rule ~~(z::_isone) * ~~x => ~x on expression (1.0 * (0.0 + (10000.0 * (1.0 - (1.0 / (1.0 + exp(-20.0 * (t - 24.0)))))))) * α₁
[slack] <Peter J> My code does a lot of parameter-parallel ODE solving, and I'd like to do them on the GPU, but since a lot of julia is not supported on the GPU by DiffEqGPU (broadcast, matrix multiply...) would this idea work?
Given an ode described by
f, an IC
u_0 and a list of parameters
[p_1, p_2,... p_n]. Create a new ode function
big_f(du,u,p,t) , and
w_0 is a
cuarray consisting of
n times, and
f seperately to each copy of
u_0, each with a different
stillon == falsecauses the callback to not fire no matter what.