Search Results - "Malachi, Schram"

The data management of heterogeneous resources in Belle II by Schram, Malachi

“…The Belle II experiment at the SuperKEKB collider in Tsukuba, Japan, has started taking physics data in early 2018 and plans to accumulate 50 ab -1 , which is…”
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A.I. for nuclear physics by Bedaque, Paulo, Boehnlein, Amber, Cromaz, Mario, Diefenthaler, Markus, Elouadrhiri, Latifa, Horn, Tanja, Kuchera, Michelle, Lawrence, David, Lee, Dean, Lidia, Steven, McKeown, Robert, Melnitchouk, Wally, Nazarewicz, Witold, Orginos, Kostas, Roblin, Yves, Scott Smith, Michael, Schram, Malachi, Wang, Xin-Nian

“…This report is an outcome of the workshop AI for Nuclear Physics held at Thomas Jefferson National Accelerator Facility on March 4–6, 2020…”
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A comparison of machine learning surrogate models of street-scale flooding in Norfolk, Virginia by McSpadden, Diana, Goldenberg, Steven, Roy, Binata, Schram, Malachi, Goodall, Jonathan L., Richter, Heather

“…Low-lying coastal cities, exemplified by Norfolk, Virginia, face the challenge of street flooding caused by rainfall and tides, which strain transportation and…”
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Uncertainty aware anomaly detection to predict errant beam pulses in the Oak Ridge Spallation Neutron Source accelerator by Blokland, Willem, Rajput, Kishansingh, Schram, Malachi, Jeske, Torri, Ramuhalli, Pradeep, Peters, Charles, Yucesan, Yigit, Zhukov, Alexander

“…High-power particle accelerators are complex machines with thousands of pieces of equipment that are frequently running at the cutting edge of technology. In…”
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Real-time artificial intelligence for accelerator control: A study at the Fermilab Booster by St. John, Jason, Herwig, Christian, Kafkes, Diana, Mitrevski, Jovan, Pellico, William A., Perdue, Gabriel N., Quintero-Parra, Andres, Schupbach, Brian A., Seiya, Kiyomi, Tran, Nhan, Schram, Malachi, Duarte, Javier M., Huang, Yunzhi, Keller, Rachael

“…We describe a method for precisely regulating the gradient magnet power supply (GMPS) at the Fermilab Booster accelerator complex using a neural network…”
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Robust errant beam prognostics with conditional modeling for particle accelerators by Rajput, Kishansingh, Schram, Malachi, Blokland, Willem, Alanazi, Yasir, Ramuhalli, Pradeep, Zhukov, Alexander, Peters, Charles, Vilalta, Ricardo

“…Abstract Particle accelerators are complex and comprise thousands of components, with many pieces of equipment running at their peak power. Consequently, they…”
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Uncertainty aware machine-learning-based surrogate models for particle accelerators: Study at the Fermilab Booster Accelerator Complex by Schram, Malachi, Rajput, Kishansingh, NS, Karthik Somayaji, Li, Peng, St. John, Jason, Sharma, Himanshu

“…Standard deep learning methods, such as Ensemble Models, Bayesian Neural Networks, and Quantile Regression Models provide estimates of prediction uncertainties…”
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Distance preserving machine learning for uncertainty aware accelerator capacitance predictions by Goldenberg, Steven, Schram, Malachi, Rajput, Kishansingh, Britton, Thomas, Pappas, Chris, Lu, Dan, Walden, Jared, Radaideh, Majdi I, Cousineau, Sarah, Harave, Sudarshan

“…Abstract Accurate uncertainty estimations are essential for producing reliable machine learning models, especially in safety-critical applications such as…”
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Scaling studies for deep learning in Liquid Argon Time Projection Chamber event classification by Strube, Jan, Bhattacharya, Kolahal, Church, Eric, Daily, Jeff, Malachi, Schram, Charles, Siegel, Kevin, Wierman

“…Measurements in Liquid Argon Time Projection Chamber neutrino detectors feature large, high fidelity event images. Deep learning techniques have been extremely…”
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Multi-module-based CVAE to predict HVCM faults in the SNS accelerator by Alanazi, Yasir, Schram, Malachi, Rajput, Kishansingh, Goldenberg, Steven, Vidyaratne, Lasitha, Pappas, Chris, Radaideh, Majdi I., Lu, Dan, Ramuhalli, Pradeep, Cousineau, Sarah

“…We present a multi-module framework based on Conditional Variational Autoencoder (CVAE) to detect anomalies in the power signals coming from multiple High…”
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Tracking the Chemical Evolution of Iodine Species Using Recurrent Neural Networks by Bilbrey, Jenna A, Marrero, Carlos Ortiz, Sassi, Michel, Ritzmann, Andrew M, Henson, Neil J, Schram, Malachi

“…We apply recurrent neural networks (RNNs) to predict the time evolution of the concentration profile of multiple species resulting from a set of interconnected…”
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Application of Deep Learning on Integrating Prediction, Provenance, and Optimization by Schram, Malachi, Tallent, Nathan, Friese, Ryan, Singh, Alok, Altintas, Ilkay

“…In this research, we investigated two approaches to detect job anomalies and/or contention for large scale computing efforts: 1. Preemptive job scheduling…”
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Distributed Computing for the Project 8 Experiment by Schram, Malachi, Thomas, Mathew, Fox, Kevin, LaRoque, Benjamin, VanDevender, Brent, Oblath, Noah, Cowley, David

“…The Project 8 collaboration aims to measure the absolute neutrino mass or improve on the current limit by measuring the tritium beta decay electron spectrum…”
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Colloquium : Machine learning in nuclear physics by Boehnlein, Amber, Diefenthaler, Markus, Sato, Nobuo, Schram, Malachi, Ziegler, Veronique, Fanelli, Cristiano, Hjorth-Jensen, Morten, Horn, Tanja, Kuchera, Michelle P., Lee, Dean, Nazarewicz, Witold, Ostroumov, Peter, Orginos, Kostas, Poon, Alan, Wang, Xin-Nian, Scheinker, Alexander, Smith, Michael S., Pang, Long-Gang

“…Advances in machine learning methods provide tools that have broad applicability in scientific research. These techniques are being applied across the…”
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Establishing MLOps for Continual Learning in Computing Clusters by McSpadden, Diana, Jones, Mark, Mohammed, Ahmed Hossam, Hess, Bryan, Schram, Malachi

“…In our exploration of the evolving behavior of a computing cluster, we focus on building an MLOps continual learning capability to support a machine learning…”
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Optimization of Thermal Conductance at Interfaces Using Machine Learning Algorithms by Rustam, Sabiha, Schram, Malachi, Lu, Zexi, Chaka, Anne M., Rosenthal, W. Steven, Pfaendtner, Jim

“…Optimization of thermal transport across the interface of two different materials is critical to micro-/nanoscale electronic, photonic, and phononic devices…”
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Time series anomaly detection in power electronics signals with recurrent and ConvLSTM autoencoders by Radaideh, Majdi I., Pappas, Chris, Walden, Jared, Lu, Dan, Vidyaratne, Lasitha, Britton, Thomas, Rajput, Kishansingh, Schram, Malachi, Cousineau, Sarah

“…The anomalies in the high voltage converter modulator (HVCM) remain a major down time for the spallation neutron source facility, that delivers the most…”
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Robust errant beam prognostics with conditional modeling for particle accelerators by Rajput, Kishansingh, Schram, Malachi, Blokland, Willem, Alanazi, Yasir, Ramuhalli, Pradeep, Zhukov, Alexander, Peters, Charles, Vilalta, Ricardo

“…Particle accelerators are complex and comprise thousands of components, with many pieces of equipment running at their peak power. Consequently, they can fault…”
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A comparison of machine learning surrogate models of street-scale flooding in Norfolk, Virginia by McSpadden, Diana, Goldenberg, Steven, Roy, Binata, Schram, Malachi, Goodall, Jonathan L., Richter, Heather

“…Low-lying coastal cities, exemplified by Norfolk, Virginia, face the challenge of street flooding caused by rainfall and tides, which strain transportation and…”
Get full text

Distance preserving machine learning for uncertainty aware accelerator capacitance predictions by Goldenberg, Steven, Schram, Malachi, Rajput, Kishansingh, Britton, Thomas, Pappas, Chris, Lu, Dan, Walden, Jared, Radaideh, Majdi I., Cousineau, Sarah, Harave, Sudarshan

“…Abstract Accurate uncertainty estimations are essential for producing reliable machine learning models, especially in safety-critical applications such as…”
Get full text