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Dynamic, adaptive sampling during nanopore sequencing using Bayesian experimental design
Nature
Nanopore sequencers can select which DNA molecules to sequence, rejecting a molecule after analysis of a small initial part.
18 months 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
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
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
Machine-learning-based dynamic-importance sampling for adaptive multiscale simulations
Nature
Multiscale simulations are a well-accepted way to bridge the length and time scales required for scientific studies with the solution...
38 months 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
Data-driven discovery of the governing equations of dynamical systems via moving horizon optimization | Scientific ...
Nature
Discovering the governing laws underpinning physical and chemical phenomena entirely from data is a key step towards understanding and...
23 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
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
Adaptive simulations, towards interactive protein-ligand modeling | Scientific Reports
Nature
Modeling the dynamic nature of protein-ligand binding with atomistic simulations is one of the main challenges in computational biophysics,...
82 months ago