Results 51 to 60 of about 130,226 (225)

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source

Rational reasoning or adaptive behavior? Evidence from two-person beauty contest games [PDF]

Many experiments have shown that human subjects do not necessarily behave in line with game theoretic assumptions and solution concepts. The reasons for this non-conformity are multiple.
Brit Grosskopf, Rosemarie Nagel
core  

A Game-Theoretic Analysis of the Off-Switch Game

, 2017
The off-switch game is a game theoretic model of a highly intelligent robot interacting with a human. In the original paper by Hadfield-Menell et al. (2016), the analysis is not fully game-theoretic as the human is modelled as an irrational player, and ...
Böörs, Mikael, Catt, Elliot, Everitt, Tom, Hutter, Marcus, Wängberg, Tobias   +4 more
core   +1 more source

Equilibrium Selection Through Incomplete Information in Coordination Games: An Experimental Study [PDF]

, 2007
We perform an experiment on a pure coordination game with uncertainty about the payoffs. Our game is closely related to models that have been used in many macroeconomic and financial applications to solve problems of equilibrium indeterminacy.
Armenter, Roc, Cabrales, Antonio, Nagel, Rosemarie   +2 more
core   +4 more sources

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

Subject-specific Performance Information can worsen the Tragedy of the Commons: Experimental Evidence [PDF]

The main aim of this article is to investigate the behavioral consequences of the provision of subject-specific information in the group effort levels chosen by players in an experimental CPR game.
Villena, Mauricio G., Zecchetto, Franco
core   +1 more source

Discovery of Novel Materials with Giant Dielectric Constants via First‐Principles Phonon Calculations and Machine Learning

Advanced Intelligent Discovery, EarlyView.
We discovered novel materials with giant dielectric constants by combining first‐principles phonon calculations and machine learning. Screening 525 perovskites identified six candidates. RbNbO3 was synthesized under pressure and showed ε ≈ 800–1000. This validates our framework as a powerful tool for high‐performance dielectric materials discovery.
Hiroki Moriwake, Kazuki Shitara, Rie Yokoi, Takeharu Kato, Isao Tanaka, Ayako Yamamoto, Kimitoshi Murase, Yosuke Hamasaiki, Yoshitaka Ehara, Mitsuru Itoh   +9 more
wiley   +1 more source

Stability of Equilibria in Games with Procedurally Rational Players [PDF]

One approach to the modeling of bounded rationality in strategic environments is based on the dynamics of evolution and learning in games. An entirely different approach has been developed recently by Osborne and Rubinstein (1998).
Rajiv Sethi
core  

ChatCFD: A Large Language Model‐Driven Agent for End‐to‐End Computational Fluid Dynamics Automation with Structured Knowledge and Reasoning

Advanced Intelligent Discovery, EarlyView.
Chat computational fluid dynamics (CFD) introduces an large language model (LLM)‐driven agent that automates OpenFOAM simulations end‐to‐end, attaining 82.1% execution success and 68.12% physical fidelity across 315 benchmarks—far surpassing prior systems.
E Fan, Kang Hu, Zhuowen Wu, Jiangyang Ge, Jiawei Miao, Yuzhi Zhang, He Sun, Weizong Wang, Tianhan Zhang   +8 more
wiley   +1 more source

Robust Reinforcement Learning Control Framework for a Quadrotor Unmanned Aerial Vehicle Using Critic Neural Network

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
Quadrotor unmanned aerial vehicle control is critical to maintain flight safety and efficiency, especially when facing external disturbances and model uncertainties. This article presents a robust reinforcement learning control scheme to deal with these challenges.
Yu Cai, Yefeng Yang, Tao Huang, Boyang Li   +3 more
wiley   +1 more source