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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
Adaptive urban traffic signal control based on enhanced deep reinforcement learning | Scientific Reports
Nature
This paper proposes an enhanced method for traffic signal control (TSC) based on DRL. This approach utilizes dueling network and double q-learning to alleviate...
2 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
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
A dynamic Bayesian optimized active recommender system for curiosity-driven partially Human-in-the-loop automated experiments | npj Computational Materials
Nature
Optimization of experimental materials synthesis and characterization through active learning methods has been growing over the last decade,...
7 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
Machine learning coarse-grained potentials of protein thermodynamics
Nature
A generalized understanding of protein dynamics is an unsolved scientific problem, the solution of which is critical to the interpretation...
11 months ago