Accelerating Science and Engineering Discoveries Through Integrated Research Infrastructure for Experiment, Big Data, Modeling and Simulation

Foundational Methods Enabling Science in an Integrated Ecosystem .- Computational Workflow for Accelerated Molecular Design Using Quantum Chemical Simulations and Deep Learning Models.- Self-learning Data Foundation for Scientific AI.- Preconditioners for batched iterative linear solvers on GPUs.- Mobility Aware Computation Offloading Model for Edge Computing.- Science and Engineering Applications Requiring and Motivating an Integrated Ecosystem .- Machine Learning for First Principles Calculations of Material Properties for Ferromagnetic Materials.- A Vision for Coupling Operation of US Fusion Facilities with HPC Systems and the Implications for Workflows and Data Management.- At-the-edge Data Processing for Low Latency High Throughput Machine Learning Algorithms.- Implementation of a framework for deploying AI inference engines in FPGAs.- Systems and Software Advances Enabling an Integrated Science and Engineering Ecosystem .- Calvera: A Platform for the Interpretation and Analysis of Neutron Scattering Data.- Virtual Infrastructure Twins: Software Testing Platforms for Computing and Instrument Ecosystems.- The INTERSECT Open Federated Architecture for the Laboratory of the Future.- Real-Time Edge Processing During Data Acquisition.- Towards a Software Development Framework for Interconnected Science Ecosystems.- Deploying Advanced Technologies for an Integrated Science and Engineering Ecosystem .- Adrastea: An Efficient FPGA Design Environment for Heterogenous Scientific Computing and Machine Learning.- Toward an Autonomous Workflow for Bragg Peak Detection at SNS.- Industrial experience deploying heterogeneous platforms for use in multi-modal power systems design workflows.- Self-Describing Digital Assets and their applications in an Integrated Science and Engineering Ecosystem.- Simulation Workflows in Minutes, at Scale for Next-Generation HPC.- Scientific Data Challenges .- Machine Learning approaches to High Throughput Phenotyping.- SMC 2022 Data Challenge: Summit Spelunkers Solution for Challenge 2.- Usage Pattern Analysis for The Summit Login Nodes.- Finding Hidden Patterns in High Resolution Wind Flow Model Simulations.- Investigating Relationships in Environmental and Community Health: Correlations Of Environment, Urban Morphology, And Socio-Economic Factors In The Los Angeles Metropolitan Statistical Area.- Patterns and Predictions: Generative Adversarial Networks for Neighborhood Generation.