@article {69567,
	title = {Decomposing heterogeneous dynamical systems with graph neural networks},
	journal = {arXiv},
	year = {2024},
	month = {2024 Jul 27},
	abstract = {<p>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.</p>
},
	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 {69128,
	title = {Phase diversity-based wavefront sensing for fluorescence microscopy.},
	journal = {Optica},
	volume = {11},
	year = {2024},
	month = {2024 Jun 10},
	pages = {806-820},
	abstract = {<p>Fluorescence microscopy is an invaluable tool in biology, yet its performance is compromised when the wavefront of light is distorted due to optical imperfections or the refractile nature of the sample. Such optical aberrations can dramatically lower the information content of images by degrading image contrast, resolution, and signal. Adaptive optics (AO) methods can sense and subsequently cancel the aberrated wavefront, but are too complex, inefficient, slow, or expensive for routine adoption by most labs. Here we introduce a rapid, sensitive, and robust wavefront sensing scheme based on phase diversity, a method successfully deployed in astronomy but underused in microscopy. Our method enables accurate wavefront sensing to less than λ/35 root mean square (RMS) error with few measurements, and AO with no additional hardware besides a corrective element. After validating the method with simulations, we demonstrate calibration of a deformable mirror \&gt; 100-fold faster than comparable methods (corresponding to wavefront sensing on the ~100 ms scale), and sensing and subsequent correction of severe aberrations (RMS wavefront distortion exceeding λ/2), restoring diffraction-limited imaging on extended biological samples.</p>
},
	doi = {10.1364/OPTICA.518559},
	url = {https://doi.org/10.1364/OPTICA.518559},
	author = {Johnson, Courtney and Guo, Min and Schneider, Magdalena C and Su, Yijun and Khuon, Satya and Reiser, Nikolaj and Wu, Yicong and Riviere, Patrick La and Shroff, Hari}
}
