Location via proxy:
[ UP ]
[Report a bug]
[Manage cookies]
No cookies
No scripts
No ads
No referrer
Show this form
All
Books
Images
News
Maps
Videos
Shopping
Search tools
Recent
Recent
Past hour
Past 24 hours
Past week
Past month
Past year
Archives
Sorted by relevance
Sorted by relevance
Sorted by date
Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks
Nature
Predicting the evolution of systems with spatio-temporal dynamics in response to external stimuli is essential for scientific progress.
6 months ago
CLOINet: ocean state reconstructions through remote-sensing, in-situ sparse observations and deep learning
Frontiers
Combining remote-sensing data with in-situ observations to achieve a comprehensive 3D reconstruction of the ocean state presents significant challenges for...
2 months ago
Constructing custom thermodynamics using deep learning
Nature
Here we develop a platform based on a generalized Onsager principle to learn macroscopic dynamical descriptions of arbitrary stochastic dissipative systems.
8 months ago
Machine Learning at the Flatiron Institute
Simons Foundation
Researchers at Flatiron are especially interested in the core areas of deep learning, probabilistic modeling, optimization, learning theory and high...
19 months ago
Dynamic modeling and optimization of an eight bar stamping mechanism based on RBF neural network PID control
Frontiers
The results show that the combination of PID control strategy and radial basis function neural network provides a powerful tool for dynamic modeling and...
4 months ago
Automatically discovering ordinary differential equations from data with sparse regression | Communications Physics
Nature
We propose a methodology to identify dynamical laws by integrating denoising techniques to smooth the signal, sparse regression to identify the relevant...
7 months ago
Google Research, 2022 & beyond: Algorithms for efficient deep learning
Google Research
Posted by Sanjiv Kumar, VP and Google Fellow, Google Research (This is Part 4 in our series of posts covering different topical areas of...
19 months ago
An explainable machine learning-based phenomapping strategy for adaptive predictive enrichment in randomized clinical trials | npj Digital Medicine
Nature
We propose and evaluate a machine learning (ML) strategy of adaptive predictive enrichment through computational trial phenomaps to optimize RCT enrollment.
9 months ago
Adaptive physics-informed neural operator for coarse-grained non-equilibrium flows
Nature
This work proposes a new machine learning (ML)-based paradigm aiming to enhance the computational efficiency of non-equilibrium reacting flow simulations.
11 months ago
Phase retrieval in holographic data storage by expanded spectrum combined with dynamic sampling method
Nature
Phase retrieval in holographic data storage by expanded spectrum combined with dynamic sampling method is proposed, which serves to both reduce media...
10 months ago