Yu, now an associate professor at the University of California, San Diego (UCSD), is a leader in a field known as “physics-guided deep learning,” having spent years incorporating our knowledge of ...

Our methodology aims to facilitate reserve estimation and optimize stimulation strategies through the prediction of high-porous zones ... derived from the fitting formula method and the CNN deep ...

The more data it has been trained on, the more accurate it is. From machine learning and deep learning to generative AI and natural language processing, different types of AI models serve various ...

Deep operator networks (DeepONet) are a popular deep learning framework often used to solve parameterized PDEs. However, applying DeepONet to porous media presents significant challenges due to its ...

are a popular deep learning framework often used to solve parameterized PDEs. However, applying DeepONet to porous media presents significant challenges due to its limited ability to extract ...

We trained DeepHRD, a deep learning platform ... subsequent clinical action. CNN, convolutional neural network; HRD, homologous recombination deficiency; HRP, homologous recombination proficient; MIL, ...

Explore the distinctions between the related but distinct technologies of deep learning and generative AI, along with their techniques, applications, strengths, and challenges. Written by eWEEK ...

The recently published book Understanding Deep Learning by [Simon J. D. Prince] is notable not only for focusing primarily on the concepts behind Deep Learning — which should make it highly ...

CNN's Fareed Zakaria offered this take on the ... and rebuild their reputations as centers of research and learning. Rep. Elise Stefanik grilled Harvard University's president during an Education ...

Synthetic chemicals that persist indefinitely in the environment have been detected in the water of several deep wells in parts of Iowa with porous bedrock, according to the Iowa Department of ...