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Found 21 papers, 10 papers with code
Neural Operators with Localized Integral and Differential Kernels
In this work, we present a principled approach to operator learning that can capture local features under two frameworks by learning differential operators and integral operators with locally supported kernels.
A Practical Probabilistic Benchmark for AI Weather Models
We also reveal how multiple time-step loss functions, which many data-driven weather models have employed, are counter-productive: they improve deterministic metrics at the cost of increased dissipation, deteriorating probabilistic skill.
Advancing Parsimonious Deep Learning Weather Prediction using the HEALPix Mesh
We present a parsimonious deep learning weather prediction model on the Hierarchical Equal Area isoLatitude Pixelization (HEALPix) to forecast seven atmospheric variables for arbitrarily long lead times on a global approximately 110 km mesh at 3h time resolution.
ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation
The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators.
Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere
Fourier Neural Operators (FNOs) have proven to be an efficient and effective method for resolution-independent operator learning in a broad variety of application areas across scientific machine learning.
FourCastNet: Accelerating Global High-Resolution Weather Forecasting using Adaptive Fourier Neural Operators
Extreme weather amplified by climate change is causing increasingly devastating impacts across the globe.
FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators
FourCastNet accurately forecasts high-resolution, fast-timescale variables such as the surface wind speed, precipitation, and atmospheric water vapor.
MLPerf HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems
Scientific communities are increasingly adopting machine learning and deep learning models in their applications to accelerate scientific insights.
Using Machine Learning to Augment Coarse-Grid Computational Fluid Dynamics Simulations
As a proof-of-concept, we demonstrate our ML-PDE strategy on a two-dimensional turbulent (Rayleigh Number $Ra=10^9$) Rayleigh-B\'enard Convection (RBC) problem.
Hierarchical Roofline Performance Analysis for Deep Learning Applications
This paper presents a practical methodology for collecting performance data necessary to conduct hierarchical Roofline analysis on NVIDIA GPUs.
Time-Based Roofline for Deep Learning Performance Analysis
Deep learning applications are usually very compute-intensive and require a long run time for training and inference.
$F_K / F_π$ from Möbius domain-wall fermions solved on gradient-flowed HISQ ensembles
We report the results of a lattice quantum chromodynamics calculation of $F_K/F_\pi$ using M\"{o}bius domain-wall fermions computed on gradient-flowed $N_f=2+1+1$ highly-improved staggered quark (HISQ) ensembles.
High Energy Physics - Lattice High Energy Physics - Experiment High Energy Physics - Phenomenology Nuclear Theory
Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs
Uncertainty quantification for forward and inverse problems is a central challenge across physical and biomedical disciplines.
Simulating the weak death of the neutron in a femtoscale universe with near-Exascale computing
The fundamental particle theory called Quantum Chromodynamics (QCD) dictates everything about protons and neutrons, from their intrinsic properties to interactions that bind them into atomic nuclei.
High Energy Physics - Lattice Distributed, Parallel, and Cluster Computing Nuclear Theory Computational Physics C.1.4; D.1.3
Exascale Deep Learning for Climate Analytics
The Tiramisu network scales to 5300 P100 GPUs with a sustained throughput of 21. 0 PF/s and parallel efficiency of 79. 0%.
Distributed, Parallel, and Cluster Computing
A percent-level determination of the nucleon axial coupling from Quantum Chromodynamics
The $\textit{axial coupling of the nucleon}$, $g_A$, is the strength of its coupling to the $\textit{weak}$ axial current of the Standard Model of particle physics, in much the same way as the electric charge is the strength of the coupling to the electromagnetic current.
High Energy Physics - Lattice High Energy Physics - Experiment High Energy Physics - Phenomenology Nuclear Experiment Nuclear Theory
Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC
There has been considerable recent activity applying deep convolutional neural nets (CNNs) to data from particle physics experiments.
Deep Learning at 15PF: Supervised and Semi-Supervised Classification for Scientific Data
This paper presents the first, 15-PetaFLOP Deep Learning system for solving scientific pattern classification problems on contemporary HPC architectures.
Möbius domain-wall fermions on gradient-flowed dynamical HISQ ensembles
We report on salient features of a mixed lattice QCD action using valence M\"{o}bius domain-wall fermions solved on the dynamical $N_f=2+1+1$ HISQ ensembles generated by the MILC Collaboration.
High Energy Physics - Lattice High Energy Physics - Phenomenology Nuclear Theory
On the Feynman-Hellmann Theorem in Quantum Field Theory and the Calculation of Matrix Elements
The Feynman-Hellmann theorem can be derived from the long Euclidean-time limit of correlation functions determined with functional derivatives of the partition function.
High Energy Physics - Lattice High Energy Physics - Phenomenology Nuclear Theory
High-Performance I/O: HDF5 for Lattice QCD
Practitioners of lattice QCD/QFT have been some of the primary pioneer users of the state-of-the-art high-performance-computing systems, and contribute towards the stress tests of such new machines as soon as they become available.
High Energy Physics - Lattice Computational Physics
