Myocardial infarction (MI) remains a leading cause of death, effecting nearly 1 million people annually. Cardiac simulations of MI in a clinical setting can aid in our understanding of the disease and guide optimal therapies. While the accuracy of current cardiac computational models of MI have improved significantly, execution times remain prohibitively slow for clinical use. In recent years computational technology has made major advances, in large part due to the rise of machine learning (ML). Using JAX, an open source ML-library, we developed a high speed cardiac simulation platform. This platform included an inverse modeling framework for estimation of active myofiber stress. The framework was developed using experimental and modeling data from a comprehensive dataset from a single ovine heart, including pressure volume loops and diffusion tensor magnetic resonance imaging data. We also conducted simulations to incorporate heart wall compressibility based on the levels of active contraction. When using a comparable mesh to our previous ABAQUS based approach, we showed a 100x speed up in simulation time. The substantial speed improvement also facilitates more sophisticated multiphysics cardiac simulations for both research and clinical settings. The simulation time was also compared to the FEniCS open-source code and showed considerable gains as well. Ongoing work includes enabling intrinsic electrophysiology capability to enable true electromechanical based simulations.