AutoPhaseNN: unsupervised physics-aware deep learning of 3D nanoscale Bragg coherent diffraction imaging
A DL-based approach which learns to solve the phase problem in 3D X-ray Bragg coherent diffraction imaging (BCDI) without labeled data.
Surrogates
A list of surrogates we look at
A list of surrogates
Calorimeter surrogates
The Kaggle calorimeter challenge uses generative AI to produce a surrogate for the Monte Carlo calculation of a calorimeter response to an incident particle (ATLAS data at LHC calculated with GEANT4).
Virtual tissue
This surrugate simulates a virtual tissue
Cosmoflow
The CosmoFlow training application benchmark from the MLPerf HPC v0.5 benchmark suite. It involves training a 3D convolutional neural network on N-body cosmology simulation data to predict physical parameters of the universe.
Fully ionized plasma fluid model closures
The closure problem in fluid modeling is a well-known challenge to modelers aiming to accurately describe their system of interest. We will choose one of the surrogates form this application and develop a reference implementation and tutorial.
Ions in nanoconfinement
This application studies ionic structure in electrolyte solutions in nanochannels with planar uncharged surfaces and can use multiple molecular dynamics (MD) codes including LAMMPS which run on HPC supercomputers with OpenMP and MPI parallelization.
Molecule docking
Docking small molecules to a protein’s binding site is often one of the first steps for virtual screening. This application is realated to CANDLE and provides a valubale example.
miniWeatherML
A simplified weather model simulating flows such as supercells that are realistic enough to be challenging and simple enough for rapid prototyping in creating and learning about surrogates.
OSMI
We explore the relationship between certain network configurations and the performance of distributed Machine Learning systems. We build upon the Open Surrogate Model Inference (OSMI) Benchmark, a distributed inference benchmark for analyzing the performance of machine-learned surrogate models
Particle dynamics
Recurrent Neural Nets as a Particle Dynamics Integrator
PtychoNN: deep learning network for ptychographic imaging that predicts sample amplitude and phase from diffraction data.
A DL-based approach to solve the ptychography data inversion problem that learns a direct mapping from the reciprocal space data to the sample amplitude and phase.
Last modified January 29, 2024: Update _index.md (03371b3)