Results 121 to 130 of about 172,371 (266)

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

Detecting Audio Adversarial Examples in Automatic Speech Recognition Systems Using Decision Boundary Patterns. [PDF]

J Imaging, 2022
Zong W, Chow YW, Susilo W, Kim J, Le NT.
europepmc   +1 more source

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source

A decade of adversarial examples: a survey on the nature and understanding of neural network non-robustness

Компьютерная оптика
Adversarial examples, in the context of computer vision, are inputs deliberately crafted to deceive or mislead artificial neural networks. These examples exploit vulnerabilities in neural networks, resulting in minimal alterations to the original input ...
A.V. Trusov, E.E. Limonova, V.V. Arlazarov   +2 more
doaj   +1 more source

Universal adversarial examples and perturbations for quantum classifiers. [PDF]

Natl Sci Rev, 2022
Gong W, Deng DL.
europepmc   +1 more source

Toward Knowledge‐Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human–AI Synergy

Advanced Intelligent Discovery, EarlyView.
This perspective highlights how knowledge‐guided artificial intelligence can address key challenges in manufacturing inverse design, including high‐dimensional search spaces, limited data, and process constraints. It focused on three complementary pillars—expert‐guided problem definition, physics‐informed machine learning, and large language model ...
Hugon Lee, Hyeonbin Moon, Junhyeong Lee, Seunghwa Ryu   +3 more
wiley   +1 more source

Universal adversarial defense in remote sensing based on pre-trained denoising diffusion models

International Journal of Applied Earth Observations and Geoinformation
Deep neural networks (DNNs) have risen to prominence as key solutions in numerous AI applications for earth observation (AI4EO). However, their susceptibility to adversarial examples poses a critical challenge, compromising the reliability of AI4EO ...
Weikang Yu, Yonghao Xu, Pedram Ghamisi
doaj   +1 more source

Generating adversarial examples without specifying a target model. [PDF]

PeerJ Comput Sci, 2021
Yang G, Li M, Fang X, Zhang J, Liang X.
europepmc   +1 more source

A Review on Recent Trends of Bioinspired Soft Robotics: Actuators, Control Methods, Materials Selection, Sensors, Challenges, and Future Prospects

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
This article reviews the current state of bioinspired soft robotics. The article discusses soft actuators, soft sensors, materials selection, and control methods used in bioinspired soft robotics. It also highlights the challenges and future prospects of this field.
Abhirup Sarker, Tamzid Ul Islam, Md. Robiul Islam   +2 more
wiley   +1 more source

Beware the Black-Box: On the Robustness of Recent Defenses to Adversarial Examples. [PDF]

Entropy (Basel), 2021
Mahmood K, Gurevin D, van Dijk M, Nguyen PH.   +3 more
europepmc   +1 more source