High-temperature plasmas, composed of electrically charged particles, interact primarily through collective effects involving many particles at once. However, occasional collisions between just two particles can lead to significant changes, such as the creation of charged ions through electron impact, the release of immense energy in nuclear fusion reactions, and the redistribution of energy and momentum through scattering. While physically accurate models exist to describe both collective interactions and binary collisions in plasmas, they are too complex to solve directly. Instead, simplified models have traditionally been used to predict plasma behavior, including nuclear fusion processes. Recent high-performance inertial confinement fusion (ICF) experiments have produced unexpected results that differ from predictions based on these simplified models. This discrepancy suggests that a more precise, high-fidelity kinetic model is needed to fully understand and optimize fusion reactions. This research project aims to develop a novel computational approach that integrates data compression techniques, fast numerical methods, and advanced mathematical modeling to make high-fidelity plasma simulations feasible on modern supercomputers. By applying this new model to experimental data, plasma behavior can be more accurately reproduced, providing insights that could lead to the design of even more efficient ICF devices, and ultimately improving fusion technologies. Plasma is a s