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Issue 372: This artwork illustrates the core innovation of pseudodynamics+: the use of Physics-Informed Neural Networks (PINNs) to solve the 'flow equations' of stem cell differentiation. In this visualization, the cellular landscape is not a static map but a dynamic system governed by a rigorous physical law. The millions of glowing points represent the individual cells that constitute the 'mass' of a tissue. Guiding these particles are the mathematical terms of the partial differential equation (PDE), including growth, drift, and diffusion. By solving these equations directly on high-dimensional single-cell data without discretization, pseudodynamics+ allows us to see how molecular changes at the single-cell level translate into the massive, coordinated expansion and differentiation of entire populations. This 'population-aware' approach provides a quantitative bridge between molecular profiles and the physical reality of tissue development and disease.