Towards generalizable deep ptychography neural networks

• URL: http://arxiv.org/abs/2509.25104v1
• Date: Mon, 29 Sep 2025 17:38:13 GMT
• Title: Towards generalizable deep ptychography neural networks
• Authors: Albert Vong, Steven Henke, Oliver Hoidn, Hanna Ruth, Junjing Deng, Alexander Hexemer, Apurva Mehta, Arianna Gleason, Levi Hancock, Nicholas Schwarz,
• Abstract summary: We propose an unsupervised training workflow emphasizing probe learning by combining experimentally-measured probes with synthetic, procedurally generated objects.<n>This probe-centric approach enables a single physics-informed neural network to reconstruct unseen experiments across multiple beamlines.
• Score: 30.626646289754095
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: X-ray ptychography is a data-intensive imaging technique expected to become ubiquitous at next-generation light sources delivering many-fold increases in coherent flux. The need for real-time feedback under accelerated acquisition rates motivates surrogate reconstruction models like deep neural networks, which offer orders-of-magnitude speedup over conventional methods. However, existing deep learning approaches lack robustness across diverse experimental conditions. We propose an unsupervised training workflow emphasizing probe learning by combining experimentally-measured probes with synthetic, procedurally generated objects. This probe-centric approach enables a single physics-informed neural network to reconstruct unseen experiments across multiple beamlines; among the first demonstrations of multi-probe generalization. We find probe learning is equally important as in-distribution learning; models trained using this synthetic workflow achieve reconstruction fidelity comparable to those trained exclusively on experimental data, even when changing the type of synthetic training object. The proposed approach enables training of experiment-steering models that provide real-time feedback under dynamic experimental conditions.

Related papers

• Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & Beyond [61.18736646013446]
  In pursuit of a deeper understanding of its surprising behaviors, we investigate the utility of a simple yet accurate model of a trained neural network. Across three case studies, we illustrate how it can be applied to derive new empirical insights on a diverse range of prominent phenomena.
  arXiv  Detail & Related papers  (2024-10-31T22:54:34Z)
• Epistemic Modeling Uncertainty of Rapid Neural Network Ensembles for Adaptive Learning [0.0]
  A new type of neural network is presented using the rapid neural network paradigm. It is found that the proposed emulator embedded neural network trains near-instantaneously, typically without loss of prediction accuracy.
  arXiv  Detail & Related papers  (2023-09-12T22:34:34Z)
• Machine learning enabled experimental design and parameter estimation for ultrafast spin dynamics [54.172707311728885]
  We introduce a methodology that combines machine learning with Bayesian optimal experimental design (BOED) Our method employs a neural network model for large-scale spin dynamics simulations for precise distribution and utility calculations in BOED. Our numerical benchmarks demonstrate the superior performance of our method in guiding XPFS experiments, predicting model parameters, and yielding more informative measurements within limited experimental time.
  arXiv  Detail & Related papers  (2023-06-03T06:19:20Z)
• Contrastive-Signal-Dependent Plasticity: Self-Supervised Learning in Spiking Neural Circuits [61.94533459151743]
  This work addresses the challenge of designing neurobiologically-motivated schemes for adjusting the synapses of spiking networks. Our experimental simulations demonstrate a consistent advantage over other biologically-plausible approaches when training recurrent spiking networks.
  arXiv  Detail & Related papers  (2023-03-30T02:40:28Z)
• Neural Maximum A Posteriori Estimation on Unpaired Data for Motion Deblurring [87.97330195531029]
  We propose a Neural Maximum A Posteriori (NeurMAP) estimation framework for training neural networks to recover blind motion information and sharp content from unpaired data. The proposed NeurMAP is an approach to existing deblurring neural networks, and is the first framework that enables training image deblurring networks on unpaired datasets.
  arXiv  Detail & Related papers  (2022-04-26T08:09:47Z)
• Deep Active Learning by Leveraging Training Dynamics [57.95155565319465]
  We propose a theory-driven deep active learning method (dynamicAL) which selects samples to maximize training dynamics. We show that dynamicAL not only outperforms other baselines consistently but also scales well on large deep learning models.
  arXiv  Detail & Related papers  (2021-10-16T16:51:05Z)
• Causal Navigation by Continuous-time Neural Networks [108.84958284162857]
  We propose a theoretical and experimental framework for learning causal representations using continuous-time neural networks. We evaluate our method in the context of visual-control learning of drones over a series of complex tasks.
  arXiv  Detail & Related papers  (2021-06-15T17:45:32Z)
• Physical model simulator-trained neural network for computational 3D phase imaging of multiple-scattering samples [1.112751058850223]
  We develop a new model-based data normalization pre-processing procedure for homogenizing the sample contrast. We demonstrate this framework's capabilities on experimental measurements of epithelial buccal cells and Caenorhabditis elegans worms.
  arXiv  Detail & Related papers  (2021-03-29T17:43:56Z)
• Ensemble learning and iterative training (ELIT) machine learning: applications towards uncertainty quantification and automated experiment in atom-resolved microscopy [0.0]
  Deep learning has emerged as a technique of choice for rapid feature extraction across imaging disciplines. Here we explore the application of deep learning for feature extraction in atom-resolved electron microscopy. This approach both allows uncertainty into deep learning analysis and also enables automated experimental detection where retraining of network to compensate for out-of-distribution drift due to change in imaging conditions is substituted for a human operator or programmatic selection of networks from the ensemble.
  arXiv  Detail & Related papers  (2021-01-21T05:29:26Z)
• Identifying nonclassicality from experimental data using artificial neural networks [52.77024349608834]
  We train an artificial neural network to classify classical and nonclassical states from their quadrature-measurement distributions. We show that the network is able to correctly identify classical and nonclassical features from real experimental quadrature data for different states of light.
  arXiv  Detail & Related papers  (2021-01-18T15:12:47Z)
• Physics-based polynomial neural networks for one-shot learning of dynamical systems from one or a few samples [0.0]
  The paper describes practical results on both a simple pendulum and one of the largest worldwide X-ray source. It is demonstrated in practice that the proposed approach allows recovering complex physics from noisy, limited, and partial observations.
  arXiv  Detail & Related papers  (2020-05-24T09:27:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.