Results 141 to 150 of about 3,240,231 (313)

Improve Adversarial Robustness of AI Models in Remote Sensing via Data-Augmentation and Explainable-AI Methods

Remote Sensing
Artificial intelligence (AI) has made remarkable progress in recent years in remote sensing applications, including environmental monitoring, crisis management, city planning, and agriculture.
Sumaiya Tasneem, Kazi Aminul Islam
doaj   +1 more source

Physics Informed Generative Surrogate Learning for Full Wave Validated Real Time Beamforming in Programmable Metasurfaces

Advanced Electronic Materials, EarlyView.
A physics‐guided generative surrogate framework is developed for programmable metasurface beamforming. Mode‐conditioned binary state generation, aperture‐physics prediction, routed residual correction, NSGA‐II optimization, and CST validation are combined to support fast candidate screening and full‐wave beam refinement across single‐beam, dual‐beam ...
Wenqian Liu, Weidong Chen, Chang Chen, Xiang Zhang, Sensong An   +4 more
wiley   +1 more source

Scoring Black-Box Models for Adversarial Robustness

, 2022
Deep neural networks are susceptible to adversarial inputs and various methods have been proposed to defend these models against adversarial attacks under different perturbation models. The robustness of models to adversarial attacks has been analyzed by
Vora, Jian, Samala, Pranay Reddy
core  

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Leveraging Attack Non-Transferability to Boost Adversarial Robustness for Foundation Models

Applied Sciences
This paper presents a novel adversarial defense framework that strategically exploits the non-transferability of adversarial attacks across multi-modal foundation models.
Koshiro Toishi, Keisuke Maeda, Ren Togo, Takahiro Ogawa, Miki Haseyama   +4 more
doaj   +1 more source

From Top to Bottom: Manufacturing Process‐Context Aware Resolution of Energy Device Electrodes Through a 3D Diffusion Generative Model

Advanced Energy Materials, EarlyView.
The application of a generative diffusion model, enhanced with a training data augmentation pipeline retaining the manufacturing process context of electrode microstructures, leads to improved fidelity of the through‐plane tortuosity factor in the AI generated samples.
Victor Ramirez‐Camacho, Siwar Ben Hadj Ali, Maxence Desnoyers, Francisco Fernandez, Nassima Benammar, Alejandro A. Franco   +5 more
wiley   +1 more source

Holistic Adversarially Robust Pruning

CoRR
Accepted by ICLR ...
Zhao, Qi, Wressnegger, Christian
openaire   +3 more sources

Adversarial robustness for unsupervised domain adaptation

, 2021
Extensive Unsupervised Domain Adaptation (UDA) studies have shown great success in practice by learning transferable representations across a labeled source domain and an unlabeled target domain with deep models.
ZHOU, F, Hong, L, Luo, P, LI, Z, Xu, H, Bae, SH, AWAIS, W   +6 more
core   +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

ATVis: Understanding and diagnosing adversarial training processes through visual analytics

Visual Informatics
Adversarial training has emerged as a major strategy against adversarial perturbations in deep neural networks, which mitigates the issue of exploiting model vulnerabilities to generate incorrect predictions.
Fang Zhu, Xufei Zhu, Xumeng Wang, Yuxin Ma, Jieqiong Zhao   +4 more
doaj   +1 more source