Physics informed deep learning models for deeply virtual exclusive processes

Deeply virtual exclusive reactions encode the dynamics of bound partons in hadrons through 3D quantum mechanical correlation functions - the generalized parton distributions; however, there are many steps in the analysis from experimental data to information on hadron structure. Currently, there is an immediate need to develop advanced phenomenology and computational tools in preparation for the exclusive reactions program planned for the upcoming EIC. The FemtoNet framework was developed to conduct an analysis of current exclusive experiments using physics-informed deep learning models in order to quantify information loss and reconstruction through the many inverse problems encountered. The FemtoNet framework simultaneously leverages a suite of uncertainty quantification techniques to separate epistemic (reducible) and aleatoric (irreducible) errors from the analysis and properly propagate experimental uncertainty. I will demonstrate what physics-informed deep neural networks are capable of in the context of reconstructing lost information from inverse problems in exclusive scattering experiments and give prospects for the future of such a program and consequences for an EIC.