@article {69902, title = {Chromatix: A differentiable, GPU-accelerated wave-optics library}, journal = {Nat Methods}, year = {2025}, month = {2026 Jun 08}, abstract = {

Many current microscopy methods incorporate computational modeling as an integral part of the imaging process, either to solve inverse problems or optimize the optical system design itself. These methods often depend on differentiable optics simulations, yet no standardized framework exists, forcing computational optics researchers to repeatedly and independently implement simulations with limited reusability and performance. These common problems limit the potential impact of computational optics as a field. Here we present Chromatix: an open-source, graphics processing unit (GPU)-accelerated, differentiable wave-optics simulation library. Chromatix builds on JAX to democratize fast, parallelized simulation of diverse optical systems and expand the design space in computational optics. Chromatix standardizes a growing collection of optical elements and propagation methods allowing a broad range of applications, which we demonstrate here for snapshot microscopy, holography and phase retrieval. We demonstrate speed improvements of 2-6 times on a single GPU and up to 22 times on 8 GPUs.

}, doi = {10.1038/s41592-026-03121-x}, url = {https:www.doi.org/10.1038/s41592-026-03121-x}, author = {Deb, Diptodip and Both, Gert-Jan and Bezzam, Eric and Kohli, Amit and Yang, Siqi and Chaware, Amey and Allier, C{\'e}dric and Cai, Changjia and Anderberg, Geneva and Eybposh, M. Hossein and Schneider, Magdalena C. and Heintzmann, Rainer and Rivera-Sanchez, Fabrizio A. and Simmerer, Corey and Meng, Guanghan and Tormes-Vaquerano, Jovan and Han, SeungYun and Shanmugavel, Sibi Chakravarthy and Maruvada, Teja and Yang, Xi and Kim, Yewon and Diederich, Benedict and Joo, Chulmin and Waller, Laura and Durr, Nicholas J. and P{\'e}gard, Nicolas C. and La Rivi{\`e}re, Patrick J. and Horstmeyer, Roarke and Chowdhury, Shwetadwip and Turaga, Srinivas C.} } @article {69567, title = {Decomposing heterogeneous dynamical systems with graph neural networks}, journal = {arXiv}, year = {2024}, month = {2024 Jul 27}, abstract = {

Natural physical, chemical, and biological dynamical systems are often complex, with heterogeneous components interacting in diverse ways. We show that graph neural networks can be designed to jointly learn the interaction rules and the structure of the heterogeneity from data alone. The learned latent structure and dynamics can be used to virtually decompose the complex system which is necessary to parameterize and infer the underlying governing equations. We tested the approach with simulation experiments of moving particles and vector fields that interact with each other. While our current aim is to better understand and validate the approach with simulated data, we anticipate it to become a generally applicable tool to uncover the governing rules underlying complex dynamics observed in nature.

}, doi = {10.48550/arXiv.2407.19160}, url = {https://doi.org/10.48550/arXiv.2407.19160}, author = {Allier, C{\'e}dric and Schneider, Magdalena C and Innerberger, Michael and Heinrich, Larissa and Bogovic, John A and Saalfeld, Stephan} } @article {69317, title = {Detecting abnormal cell behaviors from dry mass time series}, journal = {Scientific Reports}, volume = {14}, year = {2024}, month = {2024 Mar 20}, abstract = {

The prediction of pathological changes on single cell behaviour is a challenging task for deep learning models. Indeed, in self-supervised learning methods, no prior labels are used for the training and all of the information for event predictions are extracted from the data themselves. We present here a novel self-supervised learning model for the detection of anomalies in a given cell population, StArDusTS. Cells are monitored over time, and analysed to extract time-series of dry mass values. We assessed its performances on different cell lines, showing a precision of 96\% in the automatic detection of anomalies. Additionally, anomaly detection was also associated with cell measurement errors inherent to the acquisition or analysis pipelines, leading to an improvement of the upstream methods for feature extraction. Our results pave the way to novel architectures for the continuous monitoring of cell cultures in applied research or bioproduction applications, and for the prediction of pathological cellular changes.

}, doi = {10.1038/s41598-024-57684-w}, url = {https://www.nature.com/articles/s41598-024-57684-whttps://www.nature.com/articles/s41598-024-57684-w.pdfhttps://www.nature.com/articles/s41598-024-57684-w.pdfhttps://www.nature.com/articles/s41598-024-57684-w}, author = {Bailly, Romain and Malfante, Marielle and Allier, C{\'e}dric and Paviolo, Chiara and Ghenim, Lamya and Padmanabhan, Kiran and Bardin, Sabine and Mars, J{\'e}r{\^o}me} }