  In this article, we show how the theory of rough paths can be used to provide
a notion of solution to a class of nonlinear stochastic PDEs of Burgers type
that exhibit too high spatial roughness for classical analytical methods to
apply. In fact, the class of SPDEs that we consider is genuinely ill-posed in
the sense that different approximations to the nonlinearity may converge to
different limits. Using rough paths theory, a pathwise notion of solution to
these SPDEs is formulated, and we show that this yields a well-posed problem,
which is stable under a large class of perturbations, including the
approximation of the rough driving noise by a mollified version and the
addition of hyperviscosity.
  We also show that under certain structural assumptions on the coefficients,
the SPDEs under consideration generate a reversible Markov semigroup with
respect to a diffusion measure that can be given explicitly.
