This project develops TASChips, an open-source, high-performance simulation tool for thermal analysis of modern microprocessors such as CPUs, GPUs, and AI-accelerators. These microprocessors are central to the nation’s scientific, economic, and technological progress, but increasing computational demands lead to serious overheating challenges that can degrade performance, reliability, and energy efficiency of these microprocessors. TASChips addresses these challenges by enabling fast and accurate prediction of chip temperature distributions, allowing researchers and engineers to design more reliable, sustainable, energy-efficient systems. Its physics-based learning algorithms deliver accurate real-time thermal modeling capabilities at resolutions comparable to direct numerical simulations (DNS) with computational speeds even faster than dynamic thermal circuits. Such capabilities allow appropriate run-time assignments and redistributions of workloads based on dynamic hot spot distributions in the microprocessors. TASChips will be freely available to the broader research community, with extensive documentation and case studies, and integrated into educational activities. The project supports national interests by enabling better computing infrastructure, engaging STEM students in research through a REU program, and promoting innovation in thermal-aware design for next-generation computing systems. TASChips integrates physics-aware reduced-order learning models revised from