TensorFlow 2.0 RC is available

tf.contrib.integrate.odeint_fixed

ODE integration on a fixed grid (with no step size control).

``````tf.contrib.integrate.odeint_fixed(
func,
y0,
t,
dt=None,
method='rk4',
name=None
)
``````

Useful in certain scenarios to avoid the overhead of adaptive step size control, e.g. when differentiation of the integration result is desired and/or the time grid is known a priori to be sufficient.

Args:

• `func`: Function that maps a Tensor holding the state `y` and a scalar Tensor `t` into a Tensor of state derivatives with respect to time.
• `y0`: N-D Tensor giving starting value of `y` at time point `t[0]`.
• `t`: 1-D Tensor holding a sequence of time points for which to solve for `y`. The initial time point should be the first element of this sequence, and each time must be larger than the previous time. May have any floating point dtype.
• `dt`: 0-D or 1-D Tensor providing time step suggestion to be used on time integration intervals in `t`. 1-D Tensor should provide values for all intervals, must have 1 less element than that of `t`. If given a 0-D Tensor, the value is interpreted as time step suggestion same for all intervals. If passed None, then time step is set to be the t[1:] - t[:-1]. Defaults to None. The actual step size is obtained by insuring an integer number of steps per interval, potentially reducing the time step.
• `method`: One of 'midpoint' or 'rk4'.
• `name`: Optional name for the resulting operation.

Returns:

• `y`: (N+1)-D tensor, where the first dimension corresponds to different time points. Contains the solved value of y for each desired time point in `t`, with the initial value `y0` being the first element along the first dimension.

Raises:

• `ValueError`: Upon caller errors.