Researchers have demonstrated a new technique that allows "self-driving laboratories" to collect at least 10 times more data ...

In joint research with the University of Tokyo (UTokyo), the National Institute of Advanced Industrial Science and Technology ...

Using an advanced Monte Carlo method, Caltech researchers found a way to tame the infinite complexity of Feynman diagrams and ...

Researchers are using machine learning, symbolic regression, and high-performance computing to explore and classify string ...

Isolation Forest detects anomalies by isolating observations. It builds binary trees (called iTrees) by recursively ...

Muscle force and joint kinematics estimation from surface electromyography (sEMG) are essential for real-time biomechanical analysis of the dynamic interplay among neural muscle stimulation, muscle ...

Although various machine learning-based methods have been proposed for condition monitoring in power elec-tronics, they are challenging to be implemented in practice due to the accuracy, data ...

Using Physics-Informed Deep Learning (PIDL) techniques (W-PINNs-DE & W-PINNs) to solve forward and inverse hydrodynamic shock-tube problems and plane stress linear elasticity boundary value problems ...

This leading textbook provides a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as ...

Add a description, image, and links to the variational-physics-informed-neural-networks topic page so that developers can more easily learn about it ...