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08/17/2023
APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service
Cross-silo privacy-preserving federated learning (PPFL) is a powerful to...
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08/15/2023
APACE: AlphaFold2 and advanced computing as a service for accelerated discovery in biophysics
The prediction of protein 3D structure from amino acid sequence is a com...
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06/27/2023
Physics-inspired spatiotemporal-graph AI ensemble for gravitational wave detection
We introduce a novel method for gravitational wave detection that combin...
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06/14/2023
GHP-MOFassemble: Diffusion modeling, high throughput screening, and molecular dynamics for rational discovery of novel metal-organic frameworks for carbon capture at scale
We introduce GHP-MOFassemble, a Generative artificial intelligence (AI),...
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02/13/2023
Magnetohydrodynamics with Physics Informed Neural Operators
We present the first application of physics informed neural operators, w...
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12/21/2022
End-to-end AI Framework for Hyperparameter Optimization, Model Training, and Interpretable Inference for Molecules and Crystals
We introduce an end-to-end computational framework that enables hyperpar...
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12/09/2022
FAIR AI Models in High Energy Physics
The findable, accessible, interoperable, and reusable (FAIR) data princi...
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09/30/2022
FAIR for AI: An interdisciplinary, international, inclusive, and diverse community building perspective
A foundational set of findable, accessible, interoperable, and reusable ...
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07/01/2022
FAIR principles for AI models, with a practical application for accelerated high energy diffraction microscopy
A concise and measurable set of FAIR (Findable, Accessible, Interoperabl...
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03/23/2022
Applications of physics informed neural operators
We present an end-to-end framework to learn partial differential equatio...
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05/13/2021
Advances in Machine and Deep Learning for Modeling and Real-time Detection of Multi-Messenger Sources
We live in momentous times. The science community is empowered with an a...
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04/20/2020
Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers
The spin distribution of binary black hole mergers contains key informat...
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03/18/2020
Convergence of Artificial Intelligence and High Performance Computing on NSF-supported Cyberinfrastructure
Significant investments to upgrade or construct large-scale scientific f...
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12/16/2019
Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiograms
Heart disease is the leading cause of death worldwide. Amongst patients ...
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11/26/2019
Enabling real-time multi-messenger astrophysics discoveries with deep learning
Multi-messenger astrophysics is a fast-growing, interdisciplinary field ...
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03/06/2019
Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders
Denoising of time domain data is a crucial task for many applications su...
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03/05/2019
Deep Learning at Scale for Gravitational Wave Parameter Estimation of Binary Black Hole Mergers
We present the first application of deep learning at scale to do gravita...
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02/01/2019
Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era
This report provides an overview of recent work that harnesses the Big D...
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01/21/2019
The Physics of Eccentric Binary Black Hole Mergers. A Numerical Relativity Perspective
Gravitational wave observations of eccentric binary black hole mergers w...
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12/05/2018
Unsupervised learning and data clustering for the construction of Galaxy Catalogs in the Dark Energy Survey
Large scale astronomical surveys continue to increase their depth and sc...
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10/06/2018
Supporting High-Performance and High-Throughput Computing for Experimental Science
The advent of experimental science facilities, instruments and observato...
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05/07/2018
Real-time regression analysis with deep convolutional neural networks
We discuss the development of novel deep learning algorithms to enable r...
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11/27/2017
Denoising Gravitational Waves using Deep Learning with Recurrent Denoising Autoencoders
Gravitational wave astronomy is a rapidly growing field of modern astrop...
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11/21/2017
Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation with LIGO Data
The recent Nobel-prize-winning detections of gravitational waves from me...
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11/20/2017
Glitch Classification and Clustering for LIGO with Deep Transfer Learning
The detection of gravitational waves with LIGO and Virgo requires a deta...
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11/16/2017
Eccentric, nonspinning, inspiral, Gaussian-process merger approximant for the detection and characterization of eccentric binary black hole mergers
We present ENIGMA, a time domain, inspiral-merger-ringdown waveform mode...
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11/16/2017
ENIGMA: Eccentric, Non-spinning, Inspiral Gaussian-process Merger Approximant for the characterization of eccentric binary black hole mergers
We present ENIGMA, a time domain, inspiral-merger-ringdown waveform mode...
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11/08/2017
Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data
The recent Nobel-prize-winning detections of gravitational waves from me...
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08/09/2017
Python Open Source Waveform Extractor (POWER): An open source, Python package to monitor and post-process numerical relativity simulations
Numerical simulations of Einstein's field equations provide unique insig...
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06/22/2017
