Results 211 to 220 of about 156,629 (308)

Dynamic confinement controls the porous-to-free convection transition. [PDF]

Proc Natl Acad Sci U S A
Schwendener DM, Noir J, Latt J, Coreixas C, Kong XZ.   +4 more
europepmc   +1 more source

A Physics Constrained Machine Learning Pipeline for Young's Modulus Prediction in Multimaterial Hyperelastic Cylinders Guided by Contact Mechanics

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, Panagiotis Krokidas, Vasileios Vavourakis, Clara Essmann, George Giannakopoulos   +4 more
wiley   +1 more source

The sinking dynamics of a solid intruder in concentrated cornstarch suspensions studied using ultra-fast magnetic resonance imaging.

Soft Matter
Kirov NK, McLaren CP, Pruessmann KP, Müller CR, Penn A.   +4 more
europepmc   +1 more source

Morphology and energetics of the wake behind a continuously swimming crucian carp at different flow velocities. [PDF]

Sci Rep
Hou Y, Wang X, He F, Wang X, Zhu L, Cai L.   +5 more
europepmc   +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, Tianyun Huang, Aming Li, Pengyu Lv, Tingting Qin, Wenhui Chen, Hui Wei, Huiling Duan   +7 more
wiley   +1 more source

Altered ciliary morphology reduces mechanosensation in a cystic kidney model as indicated by a mathematical model. [PDF]

Sci Rep
Kumamoto K, Kagami H, Saitoh S, Yamada S, Matsumoto M, Ohno N.   +5 more
europepmc   +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

Parametric Optimization of VLM Panel Discretization Using Bio-Inspired Crayfish and Aquila Algorithms Coupled with Hybrid RSM-Based Ensemble Machine Learning Surrogate Models: A Case Study. [PDF]

Biomimetics (Basel)
Eraslan Y, Şengün E.
europepmc   +1 more source

The Interoperability Challenge in DFT Workflows Across Implementations

Advanced Intelligent Discovery, EarlyView.
Interoperability and cross‐validation remain major challenges in the computational materials science. In this work, we introduce a common input/output standard that enables internal translation across multiple workflow managers—AiiDA, PerQueue, Pipeline Pilot, and SimStack—while producing results in a unified schema.
Simon K. Steensen, Tushar Singh Thakur, Manuel Dillenz, Johan M. Carlsson, Celso R. C. Rêgo, Eibar Flores, Hamidreza Hajiyani, Felix Hanke, Juan María García Lastra, Wolfgang Wenzel, Nicola Marzari, Tejs Vegge, Giovanni Pizzi, Ivano E. Castelli   +13 more
wiley   +1 more source

The role of core strength in front crawl performance: a qualitative analysis of expert coaches' perspectives. [PDF]

Front Sports Act Living
Dai J, Xu J, Dai Z.
europepmc   +1 more source