9 papers
Physics-Guided Deep Learning For High Resolution X-ray Imaging
Shao Xian Lee, Aashwin Ananda Mishra, Ariel Arnott +21
Imperfections in X-ray imaging systems can limit their performance, especially in High Energy Density (HED) or Inertial Fusion Energy (IFE)-relevant experiments that are typically…
Domain Knowledge Guided Bayesian Optimization For Autonomous Alignment Of Complex Scientific Instruments
Aashwin Mishra, Matt Seaberg, Ryan Roussel +2
Bayesian Optimization (BO) is a powerful tool for optimizing complex non-linear systems. However, its performance degrades in high-dimensional problems with tightly coupled paramet…
A unified self-supervised framework for single-frame Fresnel CDI and overlapped ptychography
Oliver Hoidn, Albert Vong, Aashwin Mishra +3
Ptychographic imaging at synchrotron and X-ray free-electron laser sources requires densely overlapping scans, which limits throughput and increases dose; extending coherent diffra…
Deep Learning Enabled Nanoscale X-ray Photoemission Electron Microscopy (nanoXPEEM)
Aashwin Mishra, Daniel Ratner, Quynh Nguyen
Understanding and manipulating two-dimensional materials for real-world applications remains challenging due to a lack of effective and high-throughput characterization techniques.…
Data Driven Drift Correction For Complex Optical Systems
Aashwin Mishra, Matt Seaberg, Ryan Roussel +4
To exploit the thousand-fold increase in spectral brightness of modern light sources, increasingly intricate experiments are being conducted that demand extremely precise beam traj…
A Start To End Machine Learning Approach To Maximize Scientific Throughput From The LCLS-II-HE
Aashwin Mishra, Matt Seaberg, Ryan Roussel +5
With the increasing brightness of Light sources, including the Diffraction-Limited brightness upgrade of APS and the high-repetition-rate upgrade of LCLS, the proposed experiments…
Uncertainty Propagation within Chained Models for Machine Learning Reconstruction of Neutrino-LAr Interactions
Daniel Douglas, Aashwin Mishra, Daniel Ratner +2
Sequential or chained models are increasingly prevalent in machine learning for scientific applications, due to their flexibility and ease of development. Chained models are partic…
Generalizing Stochastic Smoothing for Differentiation and Gradient Estimation
Felix Petersen, Christian Borgelt, Aashwin Mishra +1
We deal with the problem of gradient estimation for stochastic differentiable relaxations of algorithms, operators, simulators, and other non-differentiable functions. Stochastic s…
Uncertainty Quantification via Stable Distribution Propagation
Felix Petersen, Aashwin Mishra, Hilde Kuehne +3
We propose a new approach for propagating stable probability distributions through neural networks. Our method is based on local linearization, which we show to be an optimal appro…