Physically Consistent Parameter Inference: Transparent Machine Learning Emulation in High Energy Physics and Cosmology

Global fits in high energy physics and cosmology often face the challenge of exploring high-dimensional parameter spaces with computationally expensive or topologically complex likelihood functions. In this work, we present a Machine Learning framework designed to emulate complex, often non-Gaussian