  The intersection of physics and machine learning has given rise to the
physics-enhanced machine learning (PEML) paradigm, aiming to improve the
capabilities and reduce the individual shortcomings of data- or physics-only
methods. In this paper, the spectrum of physics-enhanced machine learning
methods, expressed across the defining axes of physics and data, is discussed
by engaging in a comprehensive exploration of its characteristics, usage, and
motivations. In doing so, we present a survey of recent applications and
developments of PEML techniques, revealing the potency of PEML in addressing
complex challenges. We further demonstrate application of select such schemes
on the simple working example of a single degree-of-freedom Duffing oscillator,
which allows to highlight the individual characteristics and motivations of
different `genres' of PEML approaches. To promote collaboration and
transparency, and to provide practical examples for the reader, the code
generating these working examples is provided alongside this paper. As a
foundational contribution, this paper underscores the significance of PEML in
pushing the boundaries of scientific and engineering research, underpinned by
the synergy of physical insights and machine learning capabilities.
