But CoolProp is a C++ library with Python bindings. You can't jax.grad through it. You can't JIT-compile a simulation loop that calls it. And the per-call overhead from the Python↔C++ boundary adds up ...
fig, ((ax1, ax2)) = matplotlib.pyplot.subplots(1, 2, sharey='row') drawIsoLines(fluid, 'Ts', 'Q', iValues=[0.0, 1.0], axis=ax1) # for predefined styles drawIsoLines ...
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