Daniel Wheeler wd15

Predicting the future state (i+1) based on the current state (i)

$$ T_{i+1} = T_i + \frac{\Delta t}{\rho(T_i) c_p(T_i)} \left[ \nabla \cdot \big( k(T_i) \nabla T_i \big) + Q_i \right] $$

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	Integrating physics-based models, surrogate models, data assimilation and AI workflow orchestration [cite: 54, 56]
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	geometry
	{
	import_mesh false ; Use built-in mesh generator
	dim 3 ; dimension of the domain
	length 1.0e-3 ; [m]
	height 1.0e-3 ; [m] In 3D, the third parameters is width
	width 1.0e-3
	length_divisions 2 ; Number of cell layers in the length direction
	height_divisions 2 ; Number of cell layers in the height direction
	width_divisions 2

	---
	crate-directory: crate1
	title: "Phase Field Simulation: Homogeneous Nucleation Benchmark No.8a"
	description: "An example of generating an ro-crate from a PFHub result, for now this is only focused on the computational platform, environment and implementation"
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	identifier: https://raw.githubusercontent.com/usnistgov/pfhub/master/benchmarks/benchmark8.ipynb
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	Phase field modeling of nucleation has a long history and is
	covered in a number of reviews (Gránásy 2002, Castro 2003, Simmons
	2004, Gránásy 2007, Warren 2009, Heo 2014, Gránásy 2019). The

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