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Studying Complex Adaptive Systems With Internal States: A Recurrence Network Approach to the Analysis of ...
Frontiers
We discuss formal, theoretical, and practical issues with the statistical analysis of multivariate time-series data that represent self-reports of human...
3 weeks ago
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,...
6 months 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.
4 months ago
A New Bayesian Methodology for Nonlinear Model Calibration in Computational Systems Biology
Frontiers
Computational modeling is a common tool to quantitatively describe biological processes. However, most model parameters are usually unknown because they...
2 weeks 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...
17 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...
5 months ago
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 Locomotion Control of a Hexapod Robot via Bio-Inspired Learning
Frontiers
In this paper, an adaptive locomotion control approach for a hexapod robot is proposed. Inspired from biological neuro control systems, a 3D two-layer...
2 weeks ago
Adaptive physics-informed neural operator for coarse-grained non-equilibrium flows | Scientific Reports
Nature
This work proposes a new machine learning (ML)-based paradigm aiming to enhance the computational efficiency of non-equilibrium reacting...
9 months ago
Dynamic modeling and optimization of an eight bar stamping mechanism based on RBF neural network PID control
Frontiers
Introduction: Modern industrial manufacturing often requires the eight-bar stamping mechanism to have high motion accuracy and stability.
2 months ago