Explore the differences between Neural ODEs, RNNs, and LSTMs, and learn when each model is best suited for sequential data analysis tasks.

Event transition tensors make NeuralODEs interpretable, enabling rigorous analysis via compact high-order differential structure.

Single-cell profiling provides snapshots of the heterogeneous states that characterise developmental processes, organ regeneration and progression towards disease in a complex landscape. The underlying trajectories are of pivotal interest, but existing methods for reconstructing cell state trajectories commonly neglect population sizes. However, snapshot experiments make it difficult to interpret cell flux because the observed trajectories are confounded by changes in overall population size. This ambiguity can lead to misinterpreting changes in proliferation or death rates as changes in cellular migration. We introduce pseudodynamics+ , a physics-informed neural network framework that solves high-dimensional flow equations on complex, branching landscapes. By integrating single-cell genomics with population dynamics, pseudodynamics+ estimates state- and time-dependent parameters of growth, differentiation, and diffusion. The model recapitulates proliferation bursts during T-cell maturation and, when applied to LARRY-barcoded data, predicts differentiation rates consistent with clonal behaviour. When applied to time-resolved persistent-labelling datasets of in vivo mouse bone marrow haematopoiesis, pseudodynamics+ reconstructs continuous tissue flows with dynamic parameters aligned with known molecular signatures. Notably, simulations revealed a previously unrecognised shift from megakaryocyte-biased to balanced progenitor output, explained by evolving fate preferences of progenitor states, as revealed by simulations leveraging our estimated dynamic parameters. Pseudodynamics+ therefore establishes a population-aware framework for reconstructing single-cell population dynamics and is available at <https://github.com/Gottgens-lab/pseudodynamics_plus>. ### Competing Interest Statement Yes there is potential Competing Interest. The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: F.J.T. consults for Immunai, CytoReason, BioTuring, Genbio and Valinor Industries, and has ownership interest in RN.AI Therapeutics, Dermagnostix, and Cellarity. The remaining authors declare no competing interests.

ArXiv link for VidStyleODE: Disentangled Video Editing via StyleGAN and NeuralODEs

ArXiv link for VidStyleODE: Disentangled Video Editing via StyleGAN and NeuralODEs

npj Digital Medicine - Towards a physics informed digital twin to predict cerebral blood flow and cerebral vascular regulation

Event transition tensors make NeuralODEs interpretable, enabling rigorous analysis via compact high-order differential structure.