Mild solution 2026-11-05
A mild solution of an evolution equation satisfies an integrated equation built from its linear evolution, rather than requiring all derivatives in the differential equation to exist pointwise. Let generate a strongly continuous strongly continuous semigroup , and consider . Whenever the integrals are defined, its mild formulation is
The final term is a stochastic convolution. The linear evolution propagates both the initial condition and each later contribution for the remaining time. This is the infinite-dimensional counterpart of variation of constants.[ref]
A deterministic heat equation already shows why the definition is useful. Take on , zero forcing, and the discontinuous initial function . The mild solution is its convolution with the heat kernel:
It is smooth for and approaches in as decreases to zero, even though the initial function lacks the derivatives appearing in the classical equation.
The word mild does not mean approximate. It specifies how the equation is interpreted. Existence, uniqueness and whether a mild solution is also a classical or distributional solution depend on the generator, initial data, nonlinear term and noise.
Stochastic convolution 2026-11-05
A stochastic convolution propagates random forcing through a linear evolution. Let be a strongly continuous semigroup on a Hilbert space , let be a Cylindrical Wiener process on a Hilbert space , and let be a bounded linear operator. The expression
is an Itô integral whenever its integrand is square integrable in the Hilbert-Schmidt operator norm. In particular,
This Itô isometry tests whether the accumulated forcing is actually -valued. The factor evolves the contribution arriving at time through the remaining time . For with deterministic initial value , the mild solution is .[ref]
For , and real Brownian motion , with ,
This is the zero-initial-value Ornstein-Uhlenbeck process: damping limits the variance despite continued forcing. With , the limit is instead. These formulas follow by integrating in the Itô isometry.
The word convolution describes the dependence on the time difference . It does not mean an ordinary integral against a differentiable noise function; Brownian motion requires a stochastic integral.