Results 251 to 260 of about 11,198,121 (288)

From Lab to Landscape: Environmental Biohybrid Robotics for Ecological Futures

Advanced Robotics Research, EarlyView.
This Perspective explores environmental biohybrid robotics, integrating living tissues, microorganisms, and insects for operation in real‐world ecosystems. It traces the leap from laboratory experiments to forests, wetlands, and urban environments and discusses key challenges, development pathways, and opportunities for ecological monitoring and ...
Miriam Filippi
wiley   +1 more source

DRIVE‐SAFE: Data‐Driven Robustness and Informed Validation for Evolving Specifications via Formal Evaluation

Advanced Robotics Research, EarlyView.
DRIVE‐SAFE evaluates learning‐based, black‐box autonomous driving policies against evolving temporal safety requirements using Signal Temporal Logic robustness metrics. It aggregates distributional robustness measures with domain‐informed weights to guide iterative retraining.
Kristy Sakano, Jianyu An, Dinesh Manocha, Huan Xu   +3 more
wiley   +1 more source

Intelligent Sky Guardians (InSkyGuard): An Aerial Robotic Swarm for Autonomous Detection and Entrapment of Rogue Multirotors

Advanced Robotics Research, EarlyView.
Intelligent Sky Guardians (InSkyGuard) is introduced as a four‐drone swarm that autonomously detects, tracks, and safely captures rogue drones using a coordinated net system. Computer vision and leader–follower control architecture enable synchronized enclosure, while integrated failsafes enhance system reliability. Validated through closed‐environment
Joshua Hastings, Levanya Dewanarayana, Iline Shaju, Haider Al‐Wizni, Sampson Marquis, Weicheng Huang, Pooya Sareh   +6 more
wiley   +1 more source

Multimodal computer vision and machine learning for predictive monitoring of planar-flow casting of amorphous steel foils. [PDF]

PLoS One
Dutta R, Kellam M, Liang D.
europepmc   +1 more source

Reduced-Feature Hybrid Machine Learning for Coal Ash Sintering-Temperature Prediction Using Chemistry and Physicochemical Descriptors. [PDF]

ACS Omega
Al-Otoom A.
europepmc   +1 more source

Physics-inspired feature engineering and explainable machine learning for tensile strength prediction of CNT-Si₃N₄ reinforced FWTPET joints. [PDF]

Front Artif Intell
Venkatachalam K, Selvaraj SK, Lokeshkumar R, Sundaramali G.   +3 more
europepmc   +1 more source

Physics-Informed and Explainable Artificial Intelligence for Nanomaterial-Based Biosensors: From Sensor Design and Signal Processing to Clinical Translation. [PDF]

Bioengineering (Basel)
Bellucci S.
europepmc   +1 more source

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Physics-informed machine learning

Nature Reviews Physics, 2021
Despite great progress in simulating multiphysics problems using the numerical discretization of partial differential equations (PDEs), one still cannot seamlessly incorporate noisy data into existing algorithms, mesh generation remains complex, and high-dimensional problems governed by parameterized PDEs cannot be tackled.
George Em Karniadakis, Ioannis G. Kevrekidis, Lu Lu, Paris Perdikaris, Sifan Wang, Liu Yang   +5 more
openaire   +1 more source

Physics-Informed Machine Learning in Prognostics and Health Management: State of the Art and Challenges

Applied Mathematical Modelling, 2023
International audiencePrognostics and health management (PHM) plays a constructive role in the equipment’s entire life health service. It has long benefited from intensive research into physics modeling and machine learning methods. However, in practice,
Khanh T P Nguyen, Jérôme Morio, Weikun Deng   +2 more
exaly   +2 more sources

When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning

Machine Learning for Computational Science and Engineering
Abstract Physics-informed machine learning (PIML), the combination of prior physics knowledge with data-driven machine learning models, has emerged as an effective means of mitigating a shortage of training data, increasing model generalizability, and ensuring physical plausibility of results.
Chuizheng Meng
exaly   +4 more sources