This work introduces a Scalable Asynchronous Generative Inverse Problem Solver (SAGIPS) for high-performance computing systems. The resulting workflow utilizes an asynchronous ring-allreduce algorithm to transfer the gradients of a Generative Adverserial Network (GAN) across multiple GPUs. Experiments with a scientific proxy application demonstrate convergence quality comparable to traditional methods and near-linear weak scaling. This novel approach holds the potential to significantly advance methods for solving complex, large-scale inverse problems.

This work studies how stresses and momentum flow operate in bound quantum systems, with special attention paid to how stresses induced by the wave function guide particle motion. This study additionally shows how mechanical form factors—which are extracted from the quantum correlation functions measured at JLab and the EIC—can be used to image forces in bound quantum systems. The hydrogen atom is used as a case study, and it is shown that the mechanical form factors allow the Coulomb force law to be mapped out.

The QuantOm has collaboration developed an ultra-fast software package called tiktaalik to perform renormalization group evolution of generalized parton distributions. The code uses finite element methods to make the evolution auto-differentiable, allowing it to be used in AI/ML frameworks, such as those being developed by QuantOm.

We analyzed and optimized the workflow for extracting parton distribution functions from high-energy electron-proton scattering data. Profiling showed 93% of single-core time was spent on phase-space density calculations, with 78% idle time in parallel runs due to load imbalance. Optimizing the DGLAP evolution solver and using a hybrid dynamic-static scheduling strategy cut idle time by 62%, delivered a 2.46x speedup per node, and improved strong scaling. Power management features like AMD Turbo Core and RAPL limited scalability, highlighting the need for thorough intra-node performance testing.