The Uncertainty-Aware Fourier Ptychography (UA-FP) framework marks a transformative milestone in computational imaging, revolutionizing the way we address system uncertainties. This innovative ...

Abstract: Machine learning stands poised to revolutionize the process of scientific discovery across various disciplines. In this talk, we will introduce a state-of-the-art scientific machine learning ...

Swift is a general-purpose programming language that is both approachable for newcomers and powerful for experts. It is used to develop everything from apps and system software to cloud services and ...

The central tendencies we currently see in the tech industry are related to the continuous expansion of cloud computing and the rapid adoption of AI. For Java development, these trends are also ...

Abstract: In this communication, a trainable theory-guided recurrent neural network (RNN) equivalent to the finite-difference-time-domain (FDTD) method is exploited to formulate electromagnetic ...

Reduced models of transport necessarily have unknown closure relations which encode higher order physics; for example, the notorious flux limiter. In this work, we present a machine learning approach ...

The success of deep learning in recent years has been fuelled by large volumes of data, such as massive image datasets, that have made purely data-driven modelling by neural networks feasible. However ...

Data-driven approaches are becoming increasingly common as problem-solving tools in many areas of science and technology. In most cases, machine learning models are the key component of these ...

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