Results 161 to 170 of about 32,763 (309)
Advanced Intelligent Discovery, EarlyView.A physics‐guided machine learning framework estimates Young's modulus in multilayered multimaterial hyperelastic cylinders using contact mechanics. A semiempirical stiffness law is embedded into a custom neural network, ensuring physically consistent predictions. Validation against experimental and numerical data on C.
Christoforos Rekatsinas +4 more
wiley +1 more source
Evolution of Physical Intelligence Across Scales
Advanced Intelligent Discovery, EarlyView.By following the evolution of physical intelligence across scales, this article shows how intelligence arises from materials, structures, physical interactions, and collectives. It establishes physical intelligence as the evolutionary foundation upon which embodied intelligence is built.
Ke Liu +7 more
wiley +1 more source
Harnessing Machine Learning to Understand and Design Disordered Solids
Advanced Intelligent Discovery, EarlyView.This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley +1 more source
Inversion of Wind-Stress Drag Coefficient in Simulating Storm Surges by Means of Regularization Technique. [PDF]
Int J Environ Res Public Health, 2019 Xu J, Zhang Y, Lv X, Liu Q.
europepmc +1 more source
Sources of Uncertainty in Atmospheric Drag: The Drag Coefficient
, 2020 Valerie Bernstein +2 more
openaire +1 more source
Drag Coefficients of Bubbles. 1st Report. Drag Coefficients of a Single Bubble in a Stagnant Liquid.
TRANSACTIONS OF THE JAPAN SOCIETY OF MECHANICAL ENGINEERS Series B, 1995 Tomiyama, Akio +2 more
openaire +2 more sources
Large‐Scale Machine Learning to Screen for Small‐Molecule Senolytics
Advanced Intelligent Discovery, EarlyView.A consistent workflow underpins all experiments in this study. A dedicated model‐selection dataset first identifies optimal hyperparameters for each algorithm. Models are then trained and rigorously evaluated on independent sets of molecules using the senolytic ratio SR. Comprehensive hyperparameter exploration across SMILES representations, task types,
Alexis Dougha +2 more
wiley +1 more source
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.This review aims to provide a broad understanding for interdisciplinary researchers in engineering and clinical applications. It addresses the development and control of magnetic actuation systems (MASs) in clinical surgeries and their revolutionary effects in multiple clinical applications.
Yingxin Huo +3 more
wiley +1 more source
A STUDY ON DRAG COEFFICIENT OF RETICULATED STRUCTURES : Drag coefficient of 2-D reticulated plates
Journal of Structural and Construction Engineering (Transactions of AIJ), 1999 Hiroshi UEDA, Eizo MARUTA, Takeshi HONGO
openaire +2 more sources
EMG‐Driven Telemetry and Inference System for Fish: Pose Reconstruction and Flow Sensing
Advanced Intelligent Systems, EarlyView.This work introduces an electromyography (EMG)‐driven telemetry framework that reconstructs body pose and infers hydrodynamic conditions in freely swimming fish. A custom 16‐channel archival system records intramuscular EMG, enabling deep‐learning models to decode joint kinematics, classify flow regimes, and reveal channel‐efficient sensing strategies.
Rahdar Hussain Afridi +7 more
wiley +1 more source