Lessons Learned for Study of Uncertainty Quantification in Cyber-Physical System Emulation
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Thorpe, Jamie Elisabeth↗
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TCF Base Technology-Specific Final Report: Engineering Enzymes for Crystalline PET Substrate
The primary objective of this project was to develop a new polyethylene terephthalate (PET) hydrolase enzyme to depolymerize relevant PET substrates for Birch Biosciences, using high-throughput protein expression, purification, and assaying systems and machine learning-guided enzyme design. As a secondary project objective, we also aimed to develop a more energy-efficient ethylene glycol (EG) recovery strategy relative to distillation.
09 BIOMASS FUELS↗
Fast Adaptive Neural Control of Resonant Extraction at Fermilab
We present the development of a machine learning (ML) based regulation system for third-order resonant beam extraction in the Mu2e experiment at Fermilab. Classical and ML-based controllers have been optimized using semi-analytic simulations and evaluated in terms of regulation performance and training efficiency. We compare several controller architectures and discuss the integration of neural control into an adaptive framework. We also present progress on surrogate models that predict the controller response given a spill intensity and controller action history. To enable real-time deployment, we report progress on implementing low-latency, edge-based inference suitable for hardware-constrained environments. Our results demonstrate the feasibility and advantages of ML-based control in managing complex, time-varying physical systems, with broader implications for accelerator operations and other domains requiring fast, adaptive regulation.
Berlioz, Jose Rene [Fermilab]↗
Advancing $otsdaq$ for Optimized Data Acquisition
High-energy physics (HEP) experiments demand data acquisition (DAQ) systems capable of orchestrating complex detector operations, high data throughput, and responsive, real-time feedback. Traditional systems often have steep learning curves, making onboarding difficult for new users. The Off-The-Shelf Data Acquisition $otsdaq$ framework was developed to address these issues by providing a modular and flexible interface that is easier to operate while remaining customizable enough for experimental setups. As the upcoming Mu2e experiment prepares for deployment, improving stability, usability, and performance has become increasingly critical. Our work enhances $otsdaq$ with features that streamline visualization, correct data metrics, improve debugging workflows, and stabilize the user interface.
Mohammed, Ali (ORCID:0009000860386626)↗
Model-form uncertainty for trustworthy predictions
Poster to be presented at IMSI workshop Uncertainty Quantification and Machine Learning for Complex Physical Systems
Bandy, Rileigh Jade [Sandia National Laboratories ↗
Securing The Future: 2026 Manufacturing & Critical Infrastructure Threat Landscape
This report outlines the current state of manufacturing weaknesses introduced by the complexities of modern environments, including cloud services and Internet of Things (IoT) devices, with particular attention paid to the unique vulnerabilities encountered by SMMs. It also highlights CyManII’s strategic initiatives and collaborative solutions to mitigate these risks and strengthen the cybersecurity posture of the manufacturing ecosystem. Utilizing data from 2025 to inform forward-looking mitigation strategies, this report provides manufacturers with a clear understanding of both current and emerging cybersecurity threats, as well as practical opportunities to strengthen their cyber ecosystems. The following sections detail key vulnerabilities and threat vectors, along with actionable mitigation strategies, many of which have been developed or piloted through CyManII-led efforts. A thorough understanding of these risks and mitigation strategies is essential for manufacturers seeking to strengthen the security and resilience of their manufacturing operations.
3D Printing↗
BOOSTR: A Dataset for Accelerator Control Systems
The Booster Operation Optimization Sequential Time-series for Regression (BOOSTR) dataset was created to provide a cycle-by-cycle time series of readings and settings from instruments and controllable devices of the Booster, Fermilab’s Rapid-Cycling Synchrotron (RCS) operating at 15 Hz. BOOSTR provides a time series from 55 device readings and settings that pertain most directly to the high-precision regulation of the Booster’s gradient magnet power supply (GMPS). To our knowledge, this is one of the first well-documented datasets of accelerator device parameters made publicly available. We are releasing it in the hopes that it can be used to demonstrate aspects of artificial intelligence for advanced control systems, such as reinforcement learning and autonomous anomaly detection.
Kafkes, Diana (ORCID:000000021716463X)↗
Extracting the Subhalo Mass Function from Strong Lens Images with Image Segmentation
Abstract Detecting substructure within strongly lensed images is a promising route to shed light on the nature of dark matter. However, it is a challenging task, which traditionally requires detailed lens modeling and source reconstruction, taking weeks to analyze each system. We use machine learning to circumvent the need for lens and source modeling and develop a neural network to both locate subhalos in an image as well as determine their mass using the technique of image segmentation. The network is trained on images with a single subhalo located near the Einstein ring across a wide range of apparent source magnitudes. The network is then able to resolve subhalos with masses m ≳ 10 8.5 M ⊙ . Training in this way allows the network to learn the gravitational lensing of light, and, remarkably, it is then able to detect entire populations of substructure, even for locations further away from the Einstein ring than those used in training. Over a wide range of the apparent source magnitude, the false-positive rate is around three false subhalos per 100 images, coming mostly from the lightest detectable subhalo for that signal-to-noise ratio. With good accuracy and a low false-positive rate, counting the number of pixels assigned to each subhalo class over multiple images allows for a measurement of the subhalo mass function (SMF). When measured over three mass bins from 10 9 –10 10 M ⊙ the SMF slope is recovered with an error of 36% for 50 images, and this improves to 10% for 1000 images with Hubble Space Telescope-like noise.
79 ASTRONOMY AND ASTROPHYSICS↗
The problem of methods in the formation of motor habit sequences by man
Formation of motor habit sequences by man
HUMAN PERFORMANCE↗
Description of the TC 125 aboard the Mercury simulator
Two distinct phases of the TC.125 aboard the Mercury Simulator were described to pilots at a meeting in 1979. A three hour "hand on" phase, during which the pilot learns to use the system, and second; a two hour evaluation phase, during which the pilot analyzes the TC.125 and pratices making typical approaches are presented.
Source record↗
Interfacing intelligent software to robotic peripherals
A paradigm for the specification of robotic behavior is proposed, based on a template of control and data elements replicated in a hierarchical lattice. Considerations from physiological psychology are proposed as control elements for the model. The resulting model allows freedom in implementational details within a symmetrical design, and should also provide a framework for evolving robotic behavior from relatively primitive to extremely complex. The model's implications for realizing learning capabilities in robotic systems are discussed, and its implementation and demonstration are described.
Orlando, N. E.↗
Heterogeneous distributed query processing: The DAVID system
The objective of the Distributed Access View Integrated Database (DAVID) project is the development of an easy to use computer system with which NASA scientists, engineers and administrators can uniformly access distributed heterogeneous databases. Basically, DAVID will be a database management system that sits alongside already existing database and file management systems. Its function is to enable users to access the data in other languages and file systems without having to learn the data manipulation languages. Given here is an outline of a talk on the DAVID project and several charts.
Jacobs, Barry E.↗
TES: A modular systems approach to expert system development for real time space applications
A major goal of the space station era is to reduce reliance on support from ground based experts. The TIMES Expert System (TES) is an application that monitors and evaluates real time data to perform fault detection and fault isolation as it would otherwise be carried out by a knowledgeable designer. The development process and primary features of the TES, the modular system and the lessons learned are discussed.
England, Brenda↗
The spiral-compact galaxy pair AM 2208-251: Computer simulations versus observations
The system AM2208-251 is a roughly edge-on spiral extending east-west with a smaller round compact E system about 60 arcsec east of the spiral nucleus along the major axis of the spiral. Bertola, Huchtmeier, and Zeilinger (1990) have presented optical spectroscopic as well as single dish 21 cm observations of this system. Their spectroscopic data show, via emission lines lambda lambda 3727-29A, a rising rotation curve near the nucleus. These spectroscopic observations may indicate a tidal interaction in the system. In order to learn more about such pairs, the authors simulated the interaction using the computer model developed by Miller (1976 a,b, 1978) and modified by the authors (Byrd 1986, 1987, 1988). To do the simulation they need an idea of the mutual orbits of the two galaxies. Their computer model is a two-dimensional polar N-body program. It consists of a self-gravitating disk of particles, within an inert axially symmetric stabilizing halo potential. The particles are distributed in a 24(radial) by 36(azimuthal) polar grid. Self consistent calculations can be done only within the grid area. The disk is modeled with a finite Mestel disk, where all the particles initially move in circular orbits with constant tangential velocities (Mestel 1963), resulting in a flat rotation curve. The gas particles in the spiral's disk, which make up 30 percent of its mass, collide in the following manner. The number of particles in each bin of the polar grid is counted every time step. If it is greater than a given critical density, all the particles in the bin collide, obtaining in the result the same velocities, equal to the average for the bin. This process produces clumps of gas particles-the star formation sites. The authors suppress the collision in the inner part of the disk (within the circle r = 6) to represent the hole seen in the gas in the nuclear bulge of spirals. They thus avoid spurious effects due to collisions in that region. They also varied the size of the collisional bins, which did not affect their conclusions.
Klaric, Mario↗
An SSME high pressure oxidizer turbopump diagnostic system using G2(TM) real-time expert system
An expert system which diagnoses various seal leakage faults in the High Pressure Oxidizer Turbopump of the SSME was developed using G2(TM) real-time expert system. Three major functions of the software were implemented: model-based data generation, real-time expert system reasoning, and real-time input/output communication. This system is proposed as one module of a complete diagnostic system for Space Shuttle Main Engine. Diagnosis of a fault is defined as the determination of its type, severity, and likelihood. Since fault diagnosis is often accomplished through the use of heuristic human knowledge, an expert system based approach was adopted as a paradigm to develop this diagnostic system. To implement this approach, a software shell which can be easily programmed to emulate the human decision process, the G2 Real-Time Expert System, was selected. Lessons learned from this implementation are discussed.
Guo, Ten-Huei↗
An SSME High Pressure Oxidizer Turbopump diagnostic system using G2 real-time expert system
An expert system which diagnoses various seal leakage faults in the High Pressure Oxidizer Turbopump of the SSME was developed using G2 real-time expert system. Three major functions of the software were implemented: model-based data generation, real-time expert system reasoning, and real-time input/output communication. This system is proposed as one module of a complete diagnostic system for the SSME. Diagnosis of a fault is defined as the determination of its type, severity, and likelihood. Since fault diagnosis is often accomplished through the use of heuristic human knowledge, an expert system based approach has been adopted as a paradigm to develop this diagnostic system. To implement this approach, a software shell which can be easily programmed to emulate the human decision process, the G2 Real-Time Expert System, was selected. Lessons learned from this implementation are discussed.
Guo, Ten-Huei↗
Martian sedimentary deposits
The objectives are characterization of flow through outflow channels, sedimentation associated with Martian outflow systems, and documentation of Martian lakes. Over the period of the grant much, but not all, of the study centered on the Maja Valles outflow. Maja served as an example in which the effects of multiple channel routing and ponding could be studied. Maja Valles also served as the test case for calculating flow through an outflow system. Applying the lessons learned in Maja Valles and comparisons and contrast required a scrutiny of other channels.
Dehon, Rene↗
Application of artificial neural networks in hydrological modeling: A case study of runoff simulation of a Himalayan glacier basin
The simulation of runoff from a Himalayan Glacier basin using an Artificial Neural Network (ANN) is presented. The performance of the ANN model is found to be superior to the Energy Balance Model and the Multiple Regression model. The RMS Error is used as the figure of merit for judging the performance of the three models, and the RMS Error for the ANN model is the latest of the three models. The ANN is faster in learning and exhibits excellent system generalization characteristics.
Buch, A. M.↗