Physics-informed computer vision: A review and perspectives
The incorporation of physical information in machine learning frameworks is opening and
transforming many application domains. Here the learning process is augmented through …
transforming many application domains. Here the learning process is augmented through …
Physics-driven synthetic data learning for biomedical magnetic resonance: The imaging physics-based data synthesis paradigm for artificial intelligence
Deep learning (DL) has driven innovation in the field of computational imaging. One of its
bottlenecks is unavailable or insufficient training data. This article reviews an emerging …
bottlenecks is unavailable or insufficient training data. This article reviews an emerging …
Correcting model misspecification in physics-informed neural networks (PINNs)
Data-driven discovery of governing equations in computational science has emerged as a
new paradigm for obtaining accurate physical models and as a possible alternative to …
new paradigm for obtaining accurate physical models and as a possible alternative to …
Elasticity imaging using physics-informed neural networks: Spatial discovery of elastic modulus and Poisson's ratio
Elasticity imaging is a technique that discovers the spatial distribution of mechanical
properties of tissue using deformation and force measurements under various loading …
properties of tissue using deformation and force measurements under various loading …
Physics-informed neural networks (PINNs) for 4D hemodynamics prediction: an investigation of optimal framework based on vascular morphology
X Zhang, B Mao, Y Che, J Kang, M Luo, A Qiao… - Computers in Biology …, 2023 - Elsevier
Hemodynamic parameters are of great significance in the clinical diagnosis and treatment of
cardiovascular diseases. However, noninvasive, real-time and accurate acquisition of …
cardiovascular diseases. However, noninvasive, real-time and accurate acquisition of …
Investigating molecular transport in the human brain from MRI with physics-informed neural networks
In recent years, a plethora of methods combining neural networks and partial differential
equations have been developed. A widely known example are physics-informed neural …
equations have been developed. A widely known example are physics-informed neural …
Artificial intelligence for partial differential equations in computational mechanics: A review
In recent years, Artificial intelligence (AI) has become ubiquitous, empowering various fields,
especially integrating artificial intelligence and traditional science (AI for Science: Artificial …
especially integrating artificial intelligence and traditional science (AI for Science: Artificial …
Hyper-acute effects of sub-concussive soccer headers on brain function and hemodynamics
Introduction Sub-concussive head impacts in soccer are drawing increasing research
attention regarding their acute and long-term effects as players may experience thousands …
attention regarding their acute and long-term effects as players may experience thousands …