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Constructing custom thermodynamics using deep learning
Nature
One of the most exciting applications of artificial intelligence is automated scientific discovery based on previously amassed data,...
7 months ago
Studying Complex Adaptive Systems With Internal States: A Recurrence Network Approach to the Analysis of Multivariate Time-Series Data Representing Self-Reports of Human Experience
Frontiers
We discuss formal, theoretical, and practical issues with the statistical analysis of multivariate time-series data that represent self-reports of human...
1 month ago
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.
5 months ago
Dynamic phasor measurement algorithm based on high-precision time synchronization
Frontiers
Ensuring the swift and precise tracking of power system signal parameters, especially the frequency, is imperative for the secure and stable operation of...
1 month ago
Machine Learning at the Flatiron Institute
Simons Foundation
In recent years machine learning has emerged as an indispensable tool for computational science. It is also an active and growing area of study throughout...
18 months ago
Automatically discovering ordinary differential equations from data with sparse regression | Communications Physics
Nature
Discovering nonlinear differential equations that describe system dynamics from empirical data is a fundamental challenge in contemporary...
6 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...
17 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...
10 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...
10 months ago
Efficient parameter generation for constrained models using MCMC
Nature
Mathematical models of complex systems rely on parameter values to produce a desired behavior. As mathematical and computational models...
10 months ago