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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.
4 months ago
Adaptive-RAG: Enhancing Large Language Models by Question-Answering Systems with Dynamic Strategy Selection for Query Complexity
MarkTechPost
Adaptive-RAG: Enhancing Large Language Models by Question-Answering Systems with Dynamic Strategy Selection for Query Complexity.
2 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,...
5 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.
3 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...
16 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
Gradient adaptive sampling and multiple temporal scale 3D CNNs for tactile object recognition
Frontiers
Tactile object recognition (TOR) is very important for the accurate perception of robots. Most of the TOR methods usually adopt uniform...
15 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
Application of data-driven surrogate models for active human model response prediction and restraint system ...
Frontiers
Surrogate models are a must-have in a scenario-based safety simulation framework to design optimally integrated safety systems for new...
14 months ago