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A Taxonomic Survey of Physics-Informed Machine Learning
Applied Sciences, 2023 Physics-informed machine learning (PIML) refers to the emerging area of extracting physically relevant solutions to complex multiscale modeling problems lacking sufficient quantity and veracity of data with learning models informed by physically relevant
Joseph Pateras +2 more
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Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management [PDF]
Scientific Reports, 2022 Avoiding over-pressurization in subsurface reservoirs is critical for applications like CO $$_2$$ 2 sequestration and wastewater injection. Managing the pressures by controlling injection/extraction are challenging because of complex heterogeneity in the
Aleksandra Pachalieva +3 more
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Physics-Informed Machine Learning—An Emerging Trend in Tribology
Lubricants, 2023 Physics-informed machine learning (PIML) has gained significant attention in various scientific fields and is now emerging in the area of tribology. By integrating physics-based knowledge into machine learning models, PIML offers a powerful tool for ...
Max Marian, Stephan Tremmel
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Increasing the Accuracy of Hourly Multi-Output Solar Power Forecast with Physics-Informed Machine Learning [PDF]
Sensors, 2022 Machine Learning (ML)-based methods have been identified as capable of providing up to one day ahead Photovoltaic (PV) power forecasts. In this research, we introduce a generic physical model of a PV system into ML predictors to forecast from one to ...
Daniel Vázquez Pombo +4 more
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Balance equations for physics-informed machine learning [PDF]
HeliyonUsing traditional machine learning (ML) methods may produce results that are inconsistent with the laws of physics. In contrast, physics-based models of complex physical, biological, or engineering systems incorporate the laws of physics as constraints ...
Sandor M. Molnar +2 more
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A Review of Physics-Informed Machine Learning in Fluid Mechanics
Energies, 2023 Physics-informed machine-learning (PIML) enables the integration of domain knowledge with machine learning (ML) algorithms, which results in higher data efficiency and more stable predictions. This provides opportunities for augmenting—and even replacing—
Pushan Sharma +3 more
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Cuffless hemodynamic monitoring with physics-informed machine learning models [PDF]
Nature CommunicationsWearable technologies have the potential to transform ambulatory and at-home hemodynamic monitoring by providing continuous assessments of cardiovascular health metrics and guiding clinical management.
Henry Crandall +15 more
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Uncovering drone intentions using control physics informed machine learning [PDF]
Communications EngineeringUnmanned Autonomous Vehicle (UAV) or drones are increasingly used across diverse application areas. Uncooperative drones do not announce their identity/flight plans and can pose a potential risk to critical infrastructures.
Adolfo Perrusquía +3 more
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Physics-informed machine learning for microscopy [PDF]
EPJ Web of Conferences, 2022 We developed a physics-informed deep neural network architecture able to achieve signal to noise ratio improvements starting from low exposure noisy data.
Xypakis Emmanouil +4 more
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Frontiers in Materials, 2023 Rare-earth elements like neodymium, terbium and dysprosium are crucial to the performance of permanent magnets used in various green-energy technologies like hybrid or electric cars.
Alexander Kovacs +26 more
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