High-Performance Computing Simulation and Data Science

Project Highlights

 
FY2021

Click on any of the project titles below to view a brief project summary,
or return to our Project Highlights page to view lists of FY21 projects in other research categories.

Project Title Project Code Principal Investigator Project Type Project Status
Heterogeneous Computing Elements: A Quantitative Evaluation 19-ERD-004 Maya Gokhale Exploratory Research Final
Automatic Design of Transport Systems Through Topology Optimization on Adaptively Refined Computational Grids 19-ERD-035 Victor Beck Exploratory Research Final
Next Generation Machine Learning 19-ERD-007 Rushil Anirudh Exploratory Research Final
Machine Learning-Driven Dynamic Four-Dimensional X-Ray Computed Tomography Reconstruction 20-FS-010 Hyojin Kim Feasibility Study Final
Scalable Multilevel Training of Large Neural Networks 19-ERD-019 Colin Ponce Exploratory Research Final
Advanced Physics Models for Particle-to-Particle Interactions 19-ERD-025 Kambiz Salari Exploratory Research Final
Reliable Linear Solvers in Unreliable Computing Environments 21-FS-007 Christopher Vogl Feasibility Study Final
The Applicability of Unit Systems to High-Performance Computing Applications 21-FS-016 Tristan Vanderbruggen Feasibility Study Final
Scalable Motif-Driven Parallel Generative Adversarial Net for Community Detection 21-FS-029 Byung Yoo Feasibility Study Final
Using a Compressed Format for Floating-Point Data as a Data Type to Create Adaptive-Precision Compressed Arrays 21-FS-028 Alyson Fox Feasibility Study Final
Efficient Reduced-Order Models for Multi-Physics Simulations 21-FS-042 Youngsoo Choi Feasibility Study Final
MADSTARE: Modeling and Analysis for Data-Starved or Ambiguous Environments 19-SI-004 Michael Schneider Strategic Initiative Final
High-Performance Parallel Simulations for Whole-Cell Modeling 19-ERD-030 Ali Navid Exploratory Research Final
Decentralized Autonomous Networks for Cooperative Estimation 20-SI-005 Ryan Goldhahn Strategic Initiative Continuing
Robust Control of Scientific Simulations with Deep Reinforcement Learning 21-SI-001 Daniel Faissol Strategic Initiative Continuing
Automated Software Integration 21-SI-005 George Todd Gamblin Strategic Initiative Continuing
Learning a Nonlinear Solver Optimized to Solve the Problem of Electron Density and Effective Atomic Number Reconstruction 21-FS-013 Kadri Aditya Mohan Feasibility Study Continuing
Hypothesis Testing via Artificial Intelligence: Generating Physically Interpretable Models of Scientific Data with Machine Learning 19-DR-003 Brenden Petersen Disruptive Research Continuing
Scientific Machine Learning at Extreme Scale: Optimal Control for Deep Learning in High-Performance Computing 21-ERD-051 Stefanie Guenther Exploratory Research Continuing
Beating Monte Carlo: The Polynomial Method in Lattice Problems 19-DR-013 Christian Scullard Disruptive Research Continuing