torchdiffeq odeint用法 Torchdiffeq 的 odeint 是一个强大的工具,用于求解常微分方程。 它基于 PyTorch 框架,为深度学习中的动态系统建模提供了便利。odeint 函数接受方程的定义和初始条件作为输入。能够处理多种类型的常微分方程,包括线性和非线性的。对于复杂的方程系统,odeint 也能高效地求解。其精度可以通过调整...
import torch from torchdiffeq import odeint def dynamics(t, y): return torch.sin(t) t0 = 0.0 t1 = 1.0 y0 = torch.tensor([0.0]) solution = odeint(dynamics, y0, torch.tensor([t0, t1])) print(solution) 运行这个脚本: bash python test_torchdiffeq.py 如果没有报错,并且输出了一个张...
odeint(func, z0, t)で微分方程式を解きます.引数は順番に関数func, 初期値y0,時刻tです. ソルバーはtで指定されている時刻でのzの値を返します.つまり,t=tensor([t0, t1, ..., tn])のとき,out = tensor([z0, z1,..., zn])となります.t[0]は初期時刻なので,出力out[0]は必ずz0...
odeint_adjoint simply wraps around odeint, but will use only O(1) memory in exchange for solving an adjoint ODE in the backward call. The biggest gotcha is that func must be a nn.Module when using the adjoint method. This is used to collect parameters of the differential equation. Differen...
Backpropagation throughodeintgoes through the internals of the solver. Note that this is not numerically stable for all solvers (but should probably be fine with the defaultdopri5method). Instead, we encourage the use of the adjoint method explained in [1], which will allow solving with as ...
Backpropagation throughodeintgoes through the internals of the solver, but this is not supported for all solvers. Instead, we encourage the use of the adjoint method explained in [1], which will allow solving with as many steps as necessary due to O(1) memory usage. ...
Backpropagation throughodeintgoes through the internals of the solver. Note that this is not numerically stable for all solvers (but should probably be fine with the defaultdopri5method). Instead, we encourage the use of the adjoint method explained in [1], which will allow solving with as ...
Backpropagation through odeint goes through the internals of the solver, but this is not supported for all solvers. Instead, we encourage the use of the adjoint method explained in [1], which will allow solving with as many steps as necessary due to O(1) memory usage....
Backpropagation throughodeintgoes through the internals of the solver. Note that this is not numerically stable for all solvers (but should probably be fine with the defaultdopri5method). Instead, we encourage the use of the adjoint method explained in [1], which will allow solving with as ...
odeint_adjointsimply wraps aroundodeint, but will use only O(1) memory in exchange for solving an adjoint ODE in the backward call. The biggestgotchais thatfuncmust be ann.Modulewhen using the adjoint method. This is used to collect parameters of the differential equation. ...