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An interdisciplinary journal combining mathematical and experimental papers on inverse problems with numerical and practical approaches to their solution.

Machine Learning: Earth is a multidisciplinary open access journal dedicated to the application of machine learning, artificial intelligence (AI) and data-driven computational methods across all areas of Earth, environmental and climate sciences including efforts to ensure a sustainable future. The journal publishes research reporting data-driven approaches that advance our knowledge of the Earth system, and of the interactions between biosphere, hydrosphere, cryosphere, atmosphere and geosphere. The journal also publishes research that presents methodological, theoretical, or conceptual advances in machine learning and AI with applications to Earth, environmental and climate science.

Machine Learning: Engineering is a multidisciplinary open access journal dedicated to the application of machine learning (ML), artificial intelligence (AI) and data-driven computational methods across all areas of engineering. The journal also publishes research that presents methodological, theoretical, or conceptual advances in machine learning and AI with applications to engineering.

Machine Learning: Health is a multidisciplinary open access journal dedicated to the application of machine learning, artificial intelligence (AI) and data-driven computational methods across healthcare and the medical, biological, clinical, and health sciences. The journal also publishes research that presents methodological, theoretical, or conceptual advances in machine learning and AI with applications to medicine and health sciences.

Serving the multidisciplinary materials community, the journal aims to publish new research work that advances the understanding and prediction of material behaviour at scales from atomistic to macroscopic through modelling and simulation.Subject coverageModelling and/or simulation across materials science that emphasizes fundamental materials issues advancing the understanding and prediction of material behaviour. Interdisciplinary research that tackles challenging and complex materials problems where the governing phenomena may span different scales of materials behaviour, with an emphasis on the development of quantitative approaches to explain and predict experimental observations. Material processing that advances the fundamental materials science and engineering underpinning the connection between processing and properties. Covering all classes of materials, and mechanical, microstructural, electronic, chemical, biological, and optical properties.

Technical areas concerned with smart materials and structures: Materials science: composites, ceramics, processing science, interface science, sensor/actuator materials, chiral materials, conducting and chiral polymers, electrochromic materials, liquid crystals, molecular-level smart materials, biomaterials. Sensing and actuation: electromagnetic, acoustic, chemical and mechanical sensing and actuation, single-measurand sensors, multiplexed multimeasurand distributed sensors and actuators, sensor/actuator signal processing, compatibility of sensors and actuators with conventional and advanced materials, smart sensors for materials and composites processing. Optics and electromagnetics: optical fibre technology, active and adaptive optical systems and components, tunable high-dielectric phase shifters, tunable surface control. Structures: smart skins for drag and turbulence control, other applications in aerospace/hydrospace structures, civil infrastructures, transportation vehicles, manufacturing equipment, repairability and maintainability. Control: structural acoustic control, distributed control, analogue and digital feedback control, real-time implementation, adaptive structure stability, damage implications for structural control. Information processing: neural networks, data processing, data visualization and reliability.