Sample high-dimensional, multimodal failure trajectories in cyber-physical systems by iteratively training a conditional denoising diffusion model proposal distribution.
The algorithmic details can be seen in this diagram:
git clone [email protected]:sisl/DiFS.git && cd DiFS
pip install .
The DiFS trainer expects users to define an evaluation function and a tensor of initial disturbances.
The evaluation function should follow the following interface:
def evaluate(x):
"""
Example evaluation function.
Takes as input a batch of disturbance trajectories as a torch tensor.
Simulates the system under test with each sample in the batch.
Returns a tuple of tensors, where the first contains robustness for each sample,
and the second contains state trajectories.
Failure corresponds to a robustness of 0.
State trajectories are used for logging purposes only.
Args:
x (torch.Tensor): The input tensor of shape (batch_size, xdim, horizon).
Returns:
torch.Tensor: The robustness associated with each sample, shape (batch_size,).
torch.Tensor: The state trajectories associated with each disturbance, shape (batch_size, sdim, horizon).
"""
# Your code hereThe initial disturbances should be a torch tensor the same shape as input to evaluate. In practice, these are often sampled from a prior distribution designed by the user.
See the /examples folder for example usage.