Results 91 to 100 of about 221,728 (275)
Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook
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 +4 more
wiley +1 more source
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 +3 more
wiley +1 more source
Adversarial Attacks Against Medical Deep Learning Systems
Samuel G. Finlayson +3 more
openalex +2 more sources
On Adversarial Examples and Stealth Attacks in Artificial Intelligence Systems [PDF]
Ivan Tyukin +2 more
openalex +1 more source
Joint Situational Assessment‐Hierarchical Decision‐Making Framework for Maneuver Intent Decisions
This study introduces a new framework for decision‐making in unmanned combat aerial vehicles (UCAVs), integrating graph convolutional networks and hierarchical reinforcement learning (HRL). The method tackles adopts a curriculum‐based training approach guided by cross‐entropy rewards.
Ruihai Chen +4 more
wiley +1 more source
Generative Adversarial User Model for Reinforcement Learning Based Recommendation System
Xinshi Chen +5 more
openalex +2 more sources
A generative adversarial network for generating realistic users using embedding from recommendation systems [PDF]
Parichat Chonwiharnphan
openalex +1 more source
AdvHat: Real-World Adversarial Attack on ArcFace Face ID System [PDF]
Stepan Komkov, Aleksandr Petiushko
openalex +1 more source
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 +2 more
wiley +1 more source
Two Improved Methods of Generating Adversarial Examples against Faster R-CNNs for Tram Environment Perception Systems [PDF]
Shize Huang +4 more
openalex +1 more source

