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Department of Energy (DOE)
Highlights
Non-intrusive inference reduced order model for fluids using deep multistep neural network
A Feynman-Kac based numerical method for the exit time probability of a class of transport problems
A sparse-grid probabilistic scheme for approximation of the runaway probability of electrons in fusion tokamak simulation
A backward Monte-Carlo method for time-dependent runaway electron simulations
A stochastic approximate gradient ascent method for Bayesian experimental design with implicit models
AdaDGS: An adaptive black-box optimization method with a nonlocal directional Gaussian smoothing gradient
A directional Gaussian smoothing optimization method for computational inverse design in nanophotonics
Accelerating reinforcement learning with a directional Gaussian smoothing evolution strategy
Scalable deep-learning-accelerated topology optimization for additively manufactured materials
A DG-IMEX method for two-moment neutrino transport: Nonlinear solvers for neutrino-matter coupling
The Minos Computing Library: efficient parallel programming for extremely heterogeneous systems
In-Depth Optimization with the OpenACC-to-FPGA Framework on an Arria 10 FPGA
Estimating Lossy Compressibility of Scientific Data Using Deep Neural Networks
Improving In Transit and In Situ Analysis and Visualization
3D Coded SUMMA: Communication-Efficient and Robust Parallel Matrix Multiplication
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