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Found 5 papers, 2 papers with code
Neural Symplectic Integrator with Hamiltonian Inductive Bias for the Gravitational $N$-body Problem
The gravitational $N$-body problem, which is fundamentally important in astrophysics to predict the motion of $N$ celestial bodies under the mutual gravity of each other, is usually solved numerically because there is no known general analytical solution for $N>2$.
Predicting atmospheric optical properties for radiative transfer computations using neural networks
In this study, we develop a machine learning-based parametrization for the gaseous optical properties by training neural networks to emulate a modern radiation parameterization (RRTMGP).
Densifying Assumed-sparse Tensors: Improving Memory Efficiency and MPI Collective Performance during Tensor Accumulation for Parallelized Training of Neural Machine Translation Models
Neural machine translation - using neural networks to translate human language - is an area of active research exploring new neuron types and network topologies with the goal of dramatically improving machine translation performance.
Event Generation and Statistical Sampling for Physics with Deep Generative Models and a Density Information Buffer
We present a study for the generation of events from a physical process with deep generative models.
High Energy Physics - Phenomenology High Energy Physics - Experiment Data Analysis, Statistics and Probability
Scale out for large minibatch SGD: Residual network training on ImageNet-1K with improved accuracy and reduced time to train
Furthermore, as datasets grow, the representation learning potential of deep networks grows as well by using more complex models.
