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We propose to develop a stochastic optimal control framework for quantifying and reducing uncertainties in deep learning by exploiting the connection between probabilistic network architectures and…
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Project Summary: We propose to develop a scalable black-box training framework for scientific machine learning (SciML) models that are non-trainable with existing automatic differentiation-based…
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The Sensei project is led by Wes Bethel from Lawrence Berkeley National Laboratory and involves participants from multiple laboratories and industries. This project takes aim at a set of…
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The PROTEAS project is a strategic response to the continuous changes in architectures and hardware that are defining the landscape for emerging ECP systems. PROTEAS is a flexible programming…
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Parallel Aggregate Persistent Storage
Papyrus is a programming system that provides features for scalable, aggregate, persistent memory. Papyrus provides a portable and scalable…
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The FASTMath SciDAC Institute develops and deploys scalable mathematical algorithms and software tools for reliable simulation of complex physical phenomena and collaborates with application…
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In 1989, the U.S. Department of Energy (DOE) established the Atmospheric Radiation Measurement (ARM) user facility. From its home within DOE’s Office of Biological and Environmental Research, ARM…
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The Toolkit for Adaptive Stochastic Modeling and Non-Intrusive ApproximatioN is a robust library for high dimensional integration and interpolation as well as parameter calibration. The code consists…
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The goal of this project is to establish a modern mathematical and statistical foundation that will enable next-generation, complex, stochastic predictive simulations. Such a foundation is critical…
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The objective of this research is to design and evaluate a new distributed data storage paradigm that unifies the traditionally distinct application views of memory- and file-based data storage into…
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US Department of Energy (DOE) leadership computing facilities are in the process of deploying extreme-scale high-performance computing (HPC) systems with the long-range goal of building exascale…
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Developing predictive tools to understand the behavior of plasma-facing components in fusion reactors. CSMD contributions include HPC implementation, uncertainty quantification, and data analysis and…
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Extreme-scale, high-performance computing (HPC) significantly advances discovery in fundamental scientific processes by enabling multiscale simulations that range from the very small, on quantum and…
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The goal of this project is to develop a high-fidelity whole device model (WDM) of magnetically confined fusion plasmas, which is urgently needed to understand and predict the performance of ITER and…
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2016