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At least 721 records · Page 40

Meta-optic accelerators for object classifiers

Rapid advances in deep learning have led to paradigm shifts in a number of fields, from medical image analysis to autonomous systems. These advances, however, have resulted in digital neural networks with large computational requirements, resulting in high energy consumption and limitations in real-time decision-making when computation resources are limited. Here, we demonstrate a meta-optic–based neural network accelerator that can off-load computationally expensive convolution operations into high-speed and low-power optics. In this architecture, metasurfaces enable both spatial multiplexing and additional information channels, such as polarization, in object classification. End-to-end design is used to co-optimize the optical and digital systems, resulting in a robust classifier that achieves 93.1% accurate classification of handwriting digits and 93.8% accuracy in classifying both the digit and its polarization state. This approach could enable compact, high-speed, and low-power image and information processing systems for a wide range of applications in machine vision and artificial intelligence.

42 ENGINEERING↗

On the nonlinear dissipative dynamics of weakly overdamped oscillators

We consider the motion of weakly overdamped linear oscillators. Weak overdamping of an oscillator is defined as a slight excess of the damping decrement over its natural frequency. Exact solutions are obtained for a certain relation between the decrement and the natural frequency and qualitatively different regimes of motion are analyzed. The threshold conditions corresponding to changes of regimes are established; one-component models with an arbitrary degree of nonlinearity are analyzed, and quadratic and cubic nonlinearities are considered in detail. If the nonlinearity in a multicomponent model is determined by a homogeneous function, transformations of the Kummer-Liouville type can be reduced to an autonomous system of second-order differential equations in the case when the relation between the decrement and the natural frequency has been established. Some integrable multicomponent models with quadratic and cubic nonlinearities are analyzed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Demonstrating Cross-Facility Data Processing at Scale with Laue Microdiffraction

In February and April 2023 live, at-scale data processing demonstrations were conducted between the Advanced Photon Source (APS), a synchrotron light source, and the Argonne Leadership Computing Facility (ALCF). These tests were run as part of a novel beamline technique: coded aperture laue micro-diffraction. This technique requires a significant amount of compute to decode appeture patterns embedded in the detector stream. An autonomous system was able to send data to ALCF during an experiment, utilize 50 nodes of the Polaris supercomputer to process 6-12 hour scans, and return the data back to the APS within 12-15 minutes behind the detector. With scan points arriving every 72 seconds, the system kept up with the beamline, potentially enabling in-experiment analysis. The data processing system utilizes Globus infrastructure and an on-demand queue to dynamically acquire nodes on Polaris. The underlying reconstruction algorithms were parallelized via MPI and accelerated with custom CUDA kernels.

Prince, Michael↗

Autonomy for Hypersonics Mission Campaign (A4H)

A4H is developing autonomous hypersonic flight vehicles that can intelligently navigate, guide, and control themselves and home-in on targets. Autonomous systems are characterized by the use of closed-loop SENSE-THINK-ACT operations to achieve their desired goals. Closing the SENSE-THINK-ACT loop onboard these systems will provide an improved ability to engage diverse targets in contested environments.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

Gaussian process for calibration and control of GlueX Central Drift Chamber

We have developed and implemented a machine learning based system to calibrate and control the GlueX Central Drift Chamber at Jefferson Lab, VA, in near real-time. The system monitors environmental and experimental conditions during data taking and uses those as inputs to a Gaussian process (GP) with learned prior. The GP predicts calibration constants in order to recommend a high voltage (HV) setting for the detector that maintains consistent detector performance (gain and resolution) throughout data taking. This approach is in stark contrast to traditional detector operations in which the detector operates at fixed HV and its calibration parameters vary quite considerably with time. Additionally, the ML based system utilizes uncertainty quantification to correct the recommended control parameters when appropriate. We will present results from the ML system autonomously during the Charged Pion Polarizability (CPP) experiment conducted in Hall D at Jefferson Lab.

McSpadden, Helen↗

Unlocking Autonomous Telescopes through Reinforcement Learning: An Offline Framework and Insights from a Case Study

Optimizing observational astronomy campaigns is becoming a complex and expensive task for next-generation telescopes, where manual planning of observations may tend to reach suboptimal results in terms of optimization.Reinforcement Learning (RL) has been well-demonstrated as a valuable approach for training autonomous systems, and it may provide the basis for self-driving telescopes capable of scanning the sky and optimizing the scheduling for astronomy campaigns.We have developed a framework for the optimization of telescope scheduling using RL techniques, based on a dataset containing data on a discrete set of sky locations that the telescope should visit, and a reward metric. We compared several RL algorithms applied to an offline simulation dataset based at the Stone Edge Observatory, considering a discrete set of sky locations to visit and using “t-effective” as a reward metric, a measure of the quality of the data.Deep Q-Networks (DQNs), belonging to the class of value-based methods, have shown remarkable success in the optimization of astronomical observations in our dataset. In the full environment, the average reward value in each state was found to be 92%±5% of the maximum possible reward, while on the test set it resulted in 87%±9% of the maximum possible reward.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Robotic Staffing Augmentation

The Robotic Augmentation & Novel Applications team at Los Alamos National Laboratory is working to produce autonomous systems to augment staffing for tasks that are tedious, dangerous, or require high precision. This is intended to reduce risks to staff, programs and facilities while also freeing up personnel resources to better support laboratory missions, particularly pit manufacturing. The primary focus of this effort is to utilize cutting edge automation to augment long duration radiation surveys, allowing RCTs to work on critical production activities.

42 ENGINEERING↗

A Data-Driven Study of Rayleigh-Taylor Transition to Turbulence [Slides]

A temporal dynamics perspective of forgetting ICs; Autonomous system of neural ODEs of 0D dynamics; Steep growth of backward integration error quantifies forgetting; Does such a system exist in reality? (a low dim. dyn. sys. shadowing a projection of a high dim. dyn. sys.); Next: minimal QOIs to identify system; Extension to PDEs of 1D dynamics of DNS.

97 MATHEMATICS AND COMPUTING↗

Event-Driven 3D Sensing Architecture

Some events happen too quickly to capture twice. Whether tracking satellites, monitoring aircraft, observing autonomous systems, or recording high speed industrial processes, imaging systems often have only one opportunity to collect the right information. If a camera is focused on the wrong distance, uses the wrong exposure, or cannot keep pace with rapid motion, important details may be lost forever. Los Alamos National Laboratory has developed an event-driven imaging architecture that preserves more information during data collection than a conventional camera. Instead of committing to a single image, the system captures data that can later be used to digitally adjust focus and exposure while reconstructing the scene in three dimensions. The result is a more flexible approach for imaging dynamic environments where conventional cameras can struggle.

42 ENGINEERING↗

Scientific programming

Scientific programming of transformations of direction fields and autonomous systems of ordinary differential equations - Optimization of electron-optical potential for star tracker

STAR TRACKER↗

Autonomous navigation accuracy using simulated horizon sensor and sun sensor observations

A relatively simple autonomous system which would use horizon crossing indicators, a sun sensor, a quartz oscillator, and a microprogrammed computer is discussed. The sensor combination is required only to effectively measure the angle between the centers of the Earth and the Sun. Simulations for a particular orbit indicate that 2 km r.m.s. orbit determination uncertainties may be expected from a system with 0.06 deg measurement uncertainty. A key finding is that knowledge of the satellite orbit plane orientation can be maintained to this level because of the annual motion of the Sun and the predictable effects of Earth oblateness. The basic system described can be updated periodically by transits of the Moon through the IR horizon crossing indicator fields of view.

Pease, G. E.↗

Geometrically derived difference formulae for the numerical integration of trajectory problems

An initial value problem for the autonomous system of ordinary differential equations dy/dt = f(y), where y is a vector, is considered. In a number of practical applications the interest lies in obtaining the curve traced by the solution y. These applications include the computation of trajectories in mechanical problems. The term 'trajectory problem' is employed to refer to these cases. Lambert and McLeod (1979) have introduced a method involving local rotation of the axes in the y-plane for the two-dimensional case. The present investigation continues the study of difference schemes specifically derived for trajectory problems. A simple geometrical way of constructing such methods is presented, and the local accuracy of the schemes is investigated. A circularly exact, fixed-step predictor-corrector algorithm is defined, and a variable-step version of a circularly exact algorithm is presented.

Mcleod, R. J. Y.↗

NASA's computer science research program

Following a major assessment of NASA's computing technology needs, a new program of computer science research has been initiated by the Agency. The program includes work in concurrent processing, management of large scale scientific databases, software engineering, reliable computing, and artificial intelligence. The program is driven by applications requirements in computational fluid dynamics, image processing, sensor data management, real-time mission control and autonomous systems. It consists of university research, in-house NASA research, and NASA's Research Institute for Advanced Computer Science (RIACS) and Institute for Computer Applications in Science and Engineering (ICASE). The overall goal is to provide the technical foundation within NASA to exploit advancing computing technology in aerospace applications.

Larsen, R. L.↗

Space station autonomy requirements

Several concepts of autonomy have emerged from space station technology and mission studies. Unifying these concepts is important to enabling the needs for and requirements of autonomous systems for a space station to be explored. One purpose of autonomy related to a space station is to offload routine, demonstrated, precisely specifiable tasks and functions from humans to machines in order to increase habitability, increase human-machine system productivity, or to decrease operational costs. Defining incremental roles, functions, and technical capabilities for autonomy leads to identification of computing systems technologies needed to enable various degrees of autonomy.

Anderson, J. L.↗