Speaker
Description
Computing observables with respect to highly oscillatory or complex-valued distributions is an important task in physics, in particular in quantum field theory. For complex distributions, however, traditional sampling algorithms either lose their straightforward applicability or are rendered inefficient by the oscillations, which cause a signal-to-noise problem that scales exponentially with the volume - the sign problem.
Physics-informed kernels [1,2] are a generative method that has been used to tame such sign problems by computing a map that connects a real-valued proper probability distribution to the targeted complex-valued distribution. In principle, stochastic processes can be used to compute these maps. However, the application of stochastic processes to complex-valued theories comes with its own difficulties related to runaway trajectories or possible convergence to wrong solutions.
In this talk, we will discuss how the physics-informed kernel framework itself can be used to evade these obstructions, allowing transport maps for complex-valued distributions to be computed safely using stochastic processes.
[1] F. Ihssen, R. Kapust, J. M. Pawlowski , arXiv:2510.26678
[2] F. Ihssen, R. Kapust, J. M. Pawlowski , arXiv:2603.03159