Speaker
Description
Extracting continuous physical quantities from sparse and noisy data is a ubiquitous inverse problem across theoretical physics. In this talk, I will present a physics-guided generative framework based on denoising diffusion probabilistic models that reformulates function reconstruction as a conditional generation (inpainting) problem. Rather than relying on a prescribed functional ansatz, the method learns a generative prior from a diverse ensemble of theoretically motivated functional forms, enabling parametrization-independent reconstructions while preserving known physical constraints. The diffusion process naturally produces posterior ensembles, providing robust uncertainty quantification even in the regime of extremely limited data. I will discuss the methodology, its generality, and its potential as a new tool for inverse problems in theoretical physics. To illustrate its versatility, I will briefly highlight applications to the reconstruction of hadronic gravitational form factors.